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C# How to: Fuzzy Blur Filter

Article Purpose

This article serves to illustrate the concepts involved in implementing a Fuzzy Blur Filter. This filter results in rendering non-photo realistic images which express a certain artistic effect.

Frog: Filter Size 19×19

Sample Source Code

This article is accompanied by a sample source code Visual Studio project which is available for download here.

Using the Sample Application

The sample source code accompanying this article includes a Windows Forms based test application. The concepts explored throughout this article can be replicated/tested using the sample application.

When executing the sample application the user interface exposes a number of configurable options:

Loading and Saving Images – Users are able to load source/input images from the local file system by clicking the Load Image button. Clicking the Save Image button allow users to save filter result images.
Filter Size – The specified filter size affects the filter intensity. Smaller filter sizes result in less blurry images being rendered, whereas larger filter sizes result in more blurry images being rendered.
Edge Factors – The contrast of fuzzy image noise expressed in resulting images depend on the specified edge factor values. Values less than one result in detected image edges being darkened and values greater than one result in detected image edges being lightened.

The following image is a screenshot of the Fuzzy Blur Filter sample application in action:

Frog: Filter Size 9×9

Fuzzy Blur Overview

The Fuzzy Blur Filter relies on the interference of image noise when performing edge detection in order to create a fuzzy effect. In addition image blurring results from performing a mean filter.

The steps involved in performing a Fuzzy Blur Filter can be described as follows:

Edge Detection and Enhancement – Using the first edge factor specified enhance image edges by performing Boolean Edge detection. Being sensitive to image noise, a fair amount of detected image edges will actually be image noise in addition to actual image edges.
Mean Filter Blur – Using the edge enhanced image created in the previous step perform a mean filter blur. The enhanced edges will be blurred since a Mean filter does not have edge preservation properties. The size of the Mean filter implemented depends on a user specified value.
Edge Detection and Enhancement – Using the Mean filter blurred image created in the previous step once again perform Boolean Edge detection, enhancing detected edges according to the second edge factor specified.

Frog: Filter Size 9×9

Mean Filter Blurring

A Mean Filter Blur, also known as a Box Blur, can be performed through image convolution. The size of the matrix/kernel implemented when preforming image convolution will be determined through user input.

Every matrix/kernel element should be set to one. The resulting value should be multiplied by a factor value equating to one divided by the matrix/kernel size. As an example, a matrix/kernel size of 3×3 can be expressed as follows:

An alternative expression can also be:

Frog: Filter Size 9×9

Boolean Edge Detection without a local threshold

When performing Boolean Edge Detection a local threshold should be implemented in order to exclude image noise. In this article we rely on the interference of image noise in order to render a fuzzy image effect. By not implementing a local threshold when performing Boolean Edge detection the sample source code ensures sufficient interference from image noise.

The steps involved in performing Boolean Edge Detection without a local threshold can be described as follows:

Calculate Neighbourhood Mean – Iterate each pixel forming part of the source/input image. Using a 3×3 matrix size calculate the mean value of the neighbourhood surrounding the pixel currently being iterated.
Create Mean comparison Matrix – Once again using a 3×3 matrix size compare each neighbourhood pixel to the newly calculated mean value. Create a temporary 3×3 size matrix, each matrix element’s value should be the result of mean comparison. Should the value expressed by a neighbourhood pixel exceed the mean value the corresponding temporary matrix element should be set to one. When the calculated mean value exceeds the value of a neighbourhood pixel the corresponding temporary matrix element should be set to zero.
Compare Edge Masks – Using sixteen predefined edge masks compare the temporary matrix created in the previous step to each edge mask. If the temporary matrix matches one of the predefined edge masks multiply the specified factor to the pixel currently being iterated.

Note: A detailed article on Boolean Edge detection implementing a local threshold can be found here: C# How to: Boolean Edge Detection

Frog: Filter Size 9×9

The sixteen predefined edge masks each represent an image edge in a different direction. The predefined edge masks can be expressed as:

Frog: Filter Size 13×13

Implementing a Mean Filter

The sample source code defines the MeanFilter method, an extension method targeting the Bitmap class. The definition listed as follows:

private static Bitmap MeanFilter(this Bitmap sourceBitmap, 
                                 int meanSize)
{
    byte[] pixelBuffer = sourceBitmap.GetByteArray(); 
    byte[] resultBuffer = new byte[pixelBuffer.Length];


    double blue = 0.0, green = 0.0, red = 0.0;
    double factor = 1.0 / (meanSize * meanSize);


    int imageStride = sourceBitmap.Width * 4;
    int filterOffset = meanSize / 2; 
    int calcOffset = 0, filterY = 0, filterX = 0; 


    for (int k = 0; k + 4 < pixelBuffer.Length; k += 4)
    {
        blue = 0; green = 0; red = 0;
        filterY = -filterOffset;
        filterX = -filterOffset;


        while (filterY <= filterOffset)
        {
            calcOffset = k + (filterX * 4) +
            (filterY * imageStride); 


            calcOffset = (calcOffset < 0 ? 0 :
            (calcOffset >= pixelBuffer.Length - 2 ?
            pixelBuffer.Length - 3 : calcOffset));


            blue += pixelBuffer[calcOffset];
            green += pixelBuffer[calcOffset + 1];
            red += pixelBuffer[calcOffset + 2];


            filterX++;


            if (filterX > filterOffset)
            {
                filterX = -filterOffset;
                filterY++;
            }
        }


        resultBuffer[k] = ClipByte(factor * blue);
        resultBuffer[k + 1] = ClipByte(factor * green);
        resultBuffer[k + 2] = ClipByte(factor * red);
        resultBuffer[k + 3] = 255;
    }


    return resultBuffer.GetImage(sourceBitmap.Width, sourceBitmap.Height);
}

Frog: Filter Size 19×19

Implementing Boolean Edge Detection

Boolean Edge detection is performed in the sample source code through the implementation of the BooleanEdgeDetectionFilter method. This method has been defined as an extension method targeting the Bitmap class.

The following code snippet provides the definition of the BooleanEdgeDetectionFilter extension method:

public static Bitmap BooleanEdgeDetectionFilter( 
       this Bitmap sourceBitmap, float edgeFactor) 
{
    byte[] pixelBuffer = sourceBitmap.GetByteArray(); 
    byte[] resultBuffer = new byte[pixelBuffer.Length]; 
    Buffer.BlockCopy(pixelBuffer, 0, resultBuffer, 
                     0, pixelBuffer.Length); 


    List<string> edgeMasks = GetBooleanEdgeMasks(); 
 
    int imageStride = sourceBitmap.Width * 4; 
    int matrixMean = 0, pixelTotal = 0; 
    int filterY = 0, filterX = 0, calcOffset = 0; 
    string matrixPatern = String.Empty; 


    for (int k = 0; k + 4 < pixelBuffer.Length; k += 4) 
    {
        matrixPatern = String.Empty; 
        matrixMean = 0; pixelTotal = 0; 
        filterY = -1; filterX = -1; 


        while (filterY < 2) 
        {
            calcOffset = k + (filterX * 4) + 
            (filterY * imageStride); 


            calcOffset = (calcOffset < 0 ? 0 : 
            (calcOffset >= pixelBuffer.Length - 2 ? 
            pixelBuffer.Length - 3 : calcOffset)); 
 
            matrixMean += pixelBuffer[calcOffset]; 
            matrixMean += pixelBuffer[calcOffset + 1]; 
            matrixMean += pixelBuffer[calcOffset + 2]; 


            filterX += 1; 


            if (filterX > 1) 
             { filterX = -1; filterY += 1; } 
        }


        matrixMean = matrixMean / 9; 
        filterY = -1; filterX = -1; 


        while (filterY < 2) 
        {
            calcOffset = k + (filterX * 4) + 
            (filterY * imageStride); 


            calcOffset = (calcOffset < 0 ? 0 : 
            (calcOffset >= pixelBuffer.Length - 2 ? 
            pixelBuffer.Length - 3 : calcOffset)); 


            pixelTotal = pixelBuffer[calcOffset]; 
            pixelTotal += pixelBuffer[calcOffset + 1]; 
            pixelTotal += pixelBuffer[calcOffset + 2]; 
 
            matrixPatern += (pixelTotal > matrixMean 
                                       ? "1" : "0"); 
            filterX += 1; 


            if (filterX > 1) 
            { filterX = -1; filterY += 1; } 
        } 


        if (edgeMasks.Contains(matrixPatern)) 
        {
            resultBuffer[k] = 
            ClipByte(resultBuffer[k] * edgeFactor); 


            resultBuffer[k + 1] = 
            ClipByte(resultBuffer[k + 1] * edgeFactor); 


            resultBuffer[k + 2] = 
            ClipByte(resultBuffer[k + 2] * edgeFactor); 
        }
    }


    return resultBuffer.GetImage(sourceBitmap.Width, sourceBitmap.Height); 
}

Frog: Filter Size 13×13

The predefined edge masks implemented in mean comparison have been wrapped by the GetBooleanEdgeMasks method. The definition as follows:

public static List<string> GetBooleanEdgeMasks() 
{
    List<string> edgeMasks = new List<string>(); 


    edgeMasks.Add("011011011"); 
    edgeMasks.Add("000111111"); 
    edgeMasks.Add("110110110"); 
    edgeMasks.Add("111111000"); 
    edgeMasks.Add("011011001"); 
    edgeMasks.Add("100110110"); 
    edgeMasks.Add("111011000"); 
    edgeMasks.Add("111110000"); 
    edgeMasks.Add("111011001"); 
    edgeMasks.Add("100110111"); 
    edgeMasks.Add("001011111"); 
    edgeMasks.Add("111110100"); 
    edgeMasks.Add("000011111"); 
    edgeMasks.Add("000110111"); 
    edgeMasks.Add("001011011"); 
    edgeMasks.Add("110110100"); 


    return edgeMasks; 
}

Frog: Filter Size 19×19

Implementing a Fuzzy Blur Filter

The FuzzyEdgeBlurFilter method serves as the implementation of a Fuzzy Blur Filter. As discussed earlier a Fuzzy Blur Filter involves enhancing image edges through Boolean Edge detection, performing a Mean Filter blur and then once again performing Boolean Edge detection. This method has been defined as an extension method targeting the Bitmap class.

The following code snippet provides the definition of the FuzzyEdgeBlurFilter method:

public static Bitmap FuzzyEdgeBlurFilter(this Bitmap sourceBitmap,  
                                         int filterSize,  
                                         float edgeFactor1,  
                                         float edgeFactor2) 
{
    return  
    sourceBitmap.BooleanEdgeDetectionFilter(edgeFactor1). 
    MeanFilter(filterSize).BooleanEdgeDetectionFilter(edgeFactor2); 
}

Frog: Filter Size 3×3

Sample Images

This article features a number of sample images. All featured images have been licensed allowing for reproduction. The following images feature as sample images:

Tyler’s Tree Frog
Attribution: LiquidGhoul. This file has been released into the public domain by its author, LiquidGhoul. This applies worldwide.
Download from Wikipedia.

Phyllobates Terribilis
Attribution: Wilfried Berns. This file is licensed under the Creative Commons Attribution-Share Alike 2.0 Germany license.
Download from Wikipedia.

Dendropsophus Microcephalus
Attribution: Brian Gratwicke. This file is licensed under the Creative Commons Attribution 2.0 Generic license.
Download from Wikipedia

Panamanian Golden Frog
Attribution: Brian Gratwicke. This file is licensed under the Creative Commons Attribution 2.0 Generic license.
Download from Wikipedia.

C# How to: Image Abstract Colours Filter

Published July 28, 2013 Augmented Reality , Blogging , C# , Code Samples , Edge Detection , Extension Methods , Graphic Filters , How to , Image Arithmetic , Image Filtering , Image Filters , Image Processing , Image Transform , Learn Everyday , Microsoft , New Version , Non-Photorealistic Rendering , Opensource , Tip , Update 4 Comments
Tags: Augmented Reality, Binary Image, Bitmap ARGB, Bitmap Filters, Bitmap.LockBits, BitmapData, C#, Code Sample, Color Averaging, Color Filters, Computer vision, Converting Images, Edge Detect, Edge Detection, Feature extraction, Graphic Filters, Image, Image Intensity, Image processing, Image Transform, Machine vision, Non-Photorealistic Rendering, Pixel Filters

Article Purpose

This article explores Abstract Colour Image filters as a process of Non-photo Realistic Image Rendering. The output images produced reflects a variety of artistic effects.

Colour Values	Red, Blue	Filter Size	9
Edge Tracing	Black	Edge Threshold	55

Sample Source Code

This article is accompanied by a sample source code Visual Studio project which is available for download here.

Colour Values	Blue	Filter Size	9
Edge Tracing	Double Intensity	Edge Threshold	60

Using the Sample Application

The sample source code that accompanies this article includes a Windows Forms based sample application. The concepts discussed in this article have been illustrated through the implementation of the sample application.

When executing the sample application, through the user interface several options are made available to the user, described as follows:

Load/Save Images – Source/input images may be loaded from the local file system through clicking the Load Image button. If desired by the user, resulting output images can be saved to the local file system through clicking the Save Image button.
Colour Channels – The colour values Red, Green and Blue can be updated or remain unchanged when applying a filter, indicated through the associated Checkboxes.
Filter Size – The level of filter intensity depends on the size of the filter. Larger filter sizes result in more intense filtering being applied. Smaller filter size result in less intense filtering being applied.
Colour Shift Type – Colour intensity values can be swapped around through selecting the Colour Shift type: Shift Left and Shift Right.
Edge Tracing Type – When applying a filter the edges forming part of the original source/input image will be expressed as part of the output image. The method in which image edges are indicated/highlighted will be determined through the type of Edge Tracing specified when applying the filter. Supported types of Edge Tracing include: Black, White, Half Intensity, Double Intensity and Inverted.
Edge Threshold – Edges forming part of the source/input image are determines through means of image edge detection implementing a threshold value. Lower threshold values result in more emphasized edges expressed within resulting images. Higher threshold values reduce edge emphasis in resulting images.

Colour Values	Green	Filter Size	9
Edge Tracing	Double Intensity	Edge Threshold	60

Colour Values	Red, Blue	Filter Size	9
Edge Tracing	Double Intensity	Edge Threshold	55

The following image is a screenshot of the Image Abstract Colour Filter sample application in action:

Abstracting Image Colours

The Abstract Colour Filter explored in this article can be considered a non-photo realistic filter. As the title implies, non-photo realistic filters transforms an input image, usually a photograph, producing a result image which visibly lacks the aspects of realism expressed in the input image. In most scenarios the objective of non-photo realistic filters can be described as using photographic images in rendering images having an animated appearance.

Colour Values	Blue	Filter Size	11
Edge Tracing	Double Intensity	Edge Threshold	60

Colour Values	Red	Filter Size	11
Edge Tracing	Double Intensity	Edge Threshold	60

The Abstract Colour Filter can be broken down into two main components: Colour Averaging and Image Edge detection. Through implementing a variety of colour averaging algorithms resulting images express abstract yet uniform colours. Abstract colours result in output images no longer appearing photo realistic, instead output images appear unconventionally augmented/artistic.

Output images express a lesser degree of image definition and detail, when compared to input images. In some scenarios output images might not be easily recognisable. In order to retain some image detail, edge/boundary detail detected from input images will be emphasised in result images.

Colour Values	Green	Filter Size	11
Edge Tracing	Double Intensity	Edge Threshold	60

Colour Values	Green, Blue	Filter Size	11
Edge Tracing	Double Intensity	Edge Threshold	75

The steps required when applying an Abstract Colour Filter can be described as follows:

Perform Edge Detection – Using the source/input image perform edge detection, producing a binary image expressing image edges in the foreground/as white.
Calculate Pixel Neighbourhood Colour Averages – Iterate each pixel forming part of the input image, inspecting the pixel’s neighbouring pixels. Calculate the sum total and average of each colour channel, Red, Green and Blue. The value of the pixel currently being iterated should be set depending to neighbourhood average.
Trace Edges within Colour Averages – Simultaneously iterate the colour average image and the edge detected image. If the pixel being iterated forms part of an edge, update the corresponding pixel in the average colour image, depending on the specified method of Edge Tracing.

Colour Values	Red, Green	Filter Size	11
Edge Tracing	Double Intensity	Edge Threshold	75

Colour Values	Red	Filter Size	11
Edge Tracing	Black	Edge Threshold	60

Implementing Pixel Neighbourhood Colour Averaging

In the sample source code neighbourhood colour averaging has been implemented through the definition of the AverageColoursFilter extension method. This method creates a new image using the source image as input. The following code snippet provides the definition:

public static Bitmap AverageColoursFilter(this Bitmap sourceBitmap,  
                                          int matrixSize,   
                                          bool applyBlue = true, 
                                          bool applyGreen = true, 
                                          bool applyRed = true, 
                                          ColorShiftType shiftType = 
                                          ColorShiftType.None)  
{
    byte[] pixelBuffer = sourceBitmap.GetByteArray(); 
    byte[] resultBuffer = new byte[pixelBuffer.Length]; 

 
    int calcOffset = 0; 
    int byteOffset = 0; 
    int blue = 0; int green = 0; int red = 0; 
    int filterOffset = (matrixSize - 1) / 2; 

 
    for (int offsetY = filterOffset; offsetY <  
        sourceBitmap.Height - filterOffset; offsetY++) 
    {
        for (int offsetX = filterOffset; offsetX <  
            sourceBitmap.Width - filterOffset; offsetX++) 
        { 
            byteOffset = offsetY * sourceBitmap.Width*4 +  
                         offsetX * 4; 

 
            blue = 0; green = 0; red = 0; 

 
            for (int filterY = -filterOffset;  
                filterY <= filterOffset; filterY++) 
            { 
                for (int filterX = -filterOffset; 
                    filterX <= filterOffset; filterX++) 
                { 
                    calcOffset = byteOffset +  
                    (filterX * 4) + 
                    (filterY * sourceBitmap.Width * 4); 

 
                    blue += pixelBuffer[calcOffset]; 
                    green += pixelBuffer[calcOffset + 1]; 
                    red += pixelBuffer[calcOffset + 2]; 
                }
            }

 
            blue = blue / matrixSize; 
            green = green / matrixSize; 
            red = red / matrixSize; 

 
            if (applyBlue == false ) 
            { blue = pixelBuffer[byteOffset]; } 

 
            if (applyGreen == false ) 
            { green = pixelBuffer[byteOffset + 1]; } 

 
            if (applyRed == false ) 
            { red = pixelBuffer[byteOffset + 2]; } 

 
            if (shiftType == ColorShiftType.None) 
            {
                resultBuffer[byteOffset] = (byte)blue; 
                resultBuffer[byteOffset + 1] = (byte)green; 
                resultBuffer[byteOffset + 2] = (byte)red; 
                resultBuffer[byteOffset + 3] = 255; 
            }
            else if (shiftType == ColorShiftType.ShiftLeft) 
            {
                resultBuffer[byteOffset] = (byte)green; 
                resultBuffer[byteOffset + 1] = (byte)red; 
                resultBuffer[byteOffset + 2] = (byte)blue; 
                resultBuffer[byteOffset + 3] = 255; 
            }
            else if (shiftType == ColorShiftType.ShiftRight) 
            { 
                resultBuffer[byteOffset] = (byte)red; 
                resultBuffer[byteOffset + 1] = (byte)blue; 
                resultBuffer[byteOffset + 2] = (byte)green; 
                resultBuffer[byteOffset + 3] = 255; 
            } 
        }
    } 

 
    Bitmap resultBitmap = new Bitmap(sourceBitmap.Width,  
                                     sourceBitmap.Height); 

 
    BitmapData resultData =  
               resultBitmap.LockBits(new Rectangle (0, 0, 
               resultBitmap.Width, resultBitmap.Height), 
               ImageLockMode.WriteOnly, 
               PixelFormat.Format32bppArgb); 

 
    Marshal.Copy(resultBuffer, 0, resultData.Scan0,  
                               resultBuffer.Length); 

 
    resultBitmap.UnlockBits(resultData); 

 
    return resultBitmap; 
}

Colour Values	Green, Blue	Filter Size	17
Edge Tracing	Black	Edge Threshold	85

Implementing Gradient Based Edge Detection

When applying an Abstract Colours Filter, one of the required steps involve image edge detection. The sample source code implements Gradient based edge detection through the definition of the GradientBasedEdgeDetectionFilter method. This method has been defined as an extension method targeting the bitmap class. The definition as follows:

public static Bitmap GradientBasedEdgeDetectionFilter( 
                        this Bitmap sourceBitmap, 
                        byte threshold = 0) 
{
    BitmapData sourceData = 
               sourceBitmap.LockBits(new Rectangle (0, 0, 
               sourceBitmap.Width, sourceBitmap.Height), 
               ImageLockMode.ReadOnly, 
               PixelFormat.Format32bppArgb); 

 
    byte[] pixelBuffer = new byte[sourceData.Stride * sourceData.Height]; 
    byte[] resultBuffer = new byte[sourceData.Stride * sourceData.Height]; 

 
    Marshal.Copy(sourceData.Scan0, pixelBuffer, 0, pixelBuffer.Length); 
    sourceBitmap.UnlockBits(sourceData); 

 
    int sourceOffset = 0, gradientValue = 0; 
    bool exceedsThreshold = false; 

 
    for (int offsetY = 1; offsetY <  
                          sourceBitmap.Height - 1; offsetY++) 
     {
        for (int offsetX = 1; offsetX <  
                              sourceBitmap.Width - 1; offsetX++) 
         { 
            sourceOffset = offsetY * sourceData.Stride + 
                           offsetX * 4; 
            gradientValue = 0; 
            exceedsThreshold = true; 

 
            // Horizontal Gradient 
            CheckThreshold(pixelBuffer, 
                           sourceOffset - 4, 
                           sourceOffset + 4, 
                           ref gradientValue, threshold, 2); 
            // Vertical Gradient 
            exceedsThreshold = 
            CheckThreshold(pixelBuffer, 
                           sourceOffset - sourceData.Stride, 
                           sourceOffset + sourceData.Stride, 
                           ref gradientValue, threshold, 2); 

 
            if (exceedsThreshold == false) 
             { 
                gradientValue = 0; 

 
                // Horizontal Gradient 
                exceedsThreshold = 
                CheckThreshold(pixelBuffer, 
                               sourceOffset - 4, 
                               sourceOffset + 4, 
                               ref gradientValue, threshold); 


                if (exceedsThreshold == false) 
                 { 
                    gradientValue = 0; 


                    // Vertical Gradient 
                    exceedsThreshold = 
                    CheckThreshold(pixelBuffer, 
                                   sourceOffset - sourceData.Stride, 
                                   sourceOffset + sourceData.Stride, 
                                   ref gradientValue, threshold); 


                    if (exceedsThreshold == false) 
                     {
                        gradientValue = 0; 


                        // Diagonal Gradient : NW-SE 
                        CheckThreshold(pixelBuffer, 
                                       sourceOffset - 4 - sourceData.Stride, 
                                       sourceOffset + 4 + sourceData.Stride, 
                                       ref gradientValue, threshold, 2); 
                        // Diagonal Gradient : NE-SW 
                        exceedsThreshold = 
                        CheckThreshold(pixelBuffer, 
                                       sourceOffset - sourceData.Stride + 4, 
                                       sourceOffset - 4 + sourceData.Stride, 
                                       ref gradientValue, threshold, 2); 


                        if (exceedsThreshold == false) 
                         {
                            gradientValue = 0; 


                            // Diagonal Gradient : NW-SE 
                            exceedsThreshold = 
                            CheckThreshold(pixelBuffer, 
                                           sourceOffset - 4 - sourceData.Stride, 
                                           sourceOffset + 4 + sourceData.Stride, 
                                           ref gradientValue, threshold); 


                            if (exceedsThreshold == false) 
                             {
                                gradientValue = 0; 


                                // Diagonal Gradient : NE-SW 
                                exceedsThreshold = 
                                CheckThreshold(pixelBuffer, 
                                               sourceOffset - sourceData.Stride + 4, 
                                               sourceOffset + sourceData.Stride - 4, 
                                               ref gradientValue, threshold); 
                             } 
                         }
                     }
                 } 
             }


            resultBuffer[sourceOffset] = (byte)(exceedsThreshold ? 255 : 0); 
            resultBuffer[sourceOffset + 1] = resultBuffer[sourceOffset]; 
            resultBuffer[sourceOffset + 2] = resultBuffer[sourceOffset]; 
            resultBuffer[sourceOffset + 3] = 255; 
         } 
     }


    Bitmap resultBitmap = new Bitmap(sourceBitmap.Width, sourceBitmap.Height); 


    BitmapData resultData = resultBitmap.LockBits(new Rectangle (0, 0, 
                            resultBitmap.Width, resultBitmap.Height), 
                            ImageLockMode.WriteOnly, PixelFormat.Format32bppArgb); 


    Marshal.Copy(resultBuffer, 0, resultData.Scan0, resultBuffer.Length); 
    resultBitmap.UnlockBits(resultData); 


    return resultBitmap; 
}

Colour Values	Red, Green	Filter Size	5
Edge Tracing	Black	Edge Threshold	85

Implementing an Abstract Colour Filter

The AbstractColorsFilter method serves as means of combining an average colour image and an edge detected image. This extension method targets the Bitmap class. The following code snippet details the definition:

public static Bitmap AbstractColorsFilter(this Bitmap sourceBitmap, 
                                          int matrixSize, 
                                          byte edgeThreshold, 
                                          bool applyBlue = true, 
                                          bool applyGreen = true, 
                                          bool applyRed = true, 
                                          EdgeTracingType edgeType =  
                                          EdgeTracingType.Black, 
                                          ColorShiftType shiftType = 
                                          ColorShiftType.None) 
{ 
    Bitmap edgeBitmap =  
    sourceBitmap.GradientBasedEdgeDetectionFilter(edgeThreshold); 

  
    Bitmap colorBitmap =  
    sourceBitmap.AverageColoursFilter(matrixSize,  
                 applyBlue, applyGreen, applyRed, shiftType); 

  
    byte[] edgeBuffer = edgeBitmap.GetByteArray(); 
    byte[] colorBuffer = colorBitmap.GetByteArray(); 
    byte[] resultBuffer = colorBitmap.GetByteArray(); 

  
    for (int k = 0; k + 4 < edgeBuffer.Length; k += 4) 
    {
        if (edgeBuffer[k] == 255) 
        { 
            switch (edgeType) 
            {
                case EdgeTracingType.Black: 
                    resultBuffer[k] = 0; 
                    resultBuffer[k+1] = 0; 
                    resultBuffer[k+2] = 0; 
                    break; 
                case EdgeTracingType.White: 
                    resultBuffer[k] = 255; 
                    resultBuffer[k+1] = 255; 
                    resultBuffer[k+2] = 255; 
                    break; 
                case EdgeTracingType.HalfIntensity: 
                    resultBuffer[k] = ClipByte(resultBuffer[k] * 0.5); 
                    resultBuffer[k + 1] = ClipByte(resultBuffer[k + 1] * 0.5); 
                    resultBuffer[k + 2] = ClipByte(resultBuffer[k + 2] * 0.5); 
                    break; 
                case EdgeTracingType.DoubleIntensity: 
                    resultBuffer[k] = ClipByte(resultBuffer[k] * 2); 
                    resultBuffer[k + 1] = ClipByte(resultBuffer[k + 1] * 2); 
                    resultBuffer[k + 2] = ClipByte(resultBuffer[k + 2] * 2); 
                    break; 
                case EdgeTracingType.ColorInversion: 
                    resultBuffer[k] = ClipByte(255 - resultBuffer[k]); 
                    resultBuffer[k+1] = ClipByte(255 - resultBuffer[k+1]); 
                    resultBuffer[k+2] = ClipByte(255 - resultBuffer[k+2]); 
                    break; 
            }
        }
    }

  
    Bitmap resultBitmap = new Bitmap (sourceBitmap.Width, 
                                    sourceBitmap.Height); 

  
    BitmapData resultData = 
               resultBitmap.LockBits(new Rectangle (0, 0, 
               resultBitmap.Width, resultBitmap.Height), 
               ImageLockMode.WriteOnly, 
               PixelFormat.Format32bppArgb); 

  
    Marshal.Copy(resultBuffer, 0, resultData.Scan0, 
                               resultBuffer.Length); 

  
    resultBitmap.UnlockBits(resultData); 

  
    return resultBitmap; 
}

Colour Values	Red, Green	Filter Size	17
Edge Tracing	Double Intensity	Edge Threshold	85

Sample Images

This article features a number of sample images. All featured images have been licensed allowing for reproduction. The following images feature as sample images:

Fruit bodies of the agaric fungus Mycena atkinsoniana A.H. Sm. Specimens photographed in Strouds Run State Park, Athens, Ohio, USA
Attributed to: Dan Molter. This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license.
Download from Wikipedia

The "indigo Lactarius", species Lactarius indigo (Schwein.) Fr. Specimen photographed in Strouds Run State Park, Athens, Ohio, USA.
Attributed to: Dan Molter. This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license.
Download from Wikipedia

Amanita muscaria (fly agaric), Norway
Attributed to: User-MichaelMaggs. This file is licensed under the Creative Commons Attribution-Share Alike 2.5 Generic license.
Download from Wikipedia.

Lung Oyster (Pleurotus pulmonarius), Småland, Sweden.
Attributed to: Jörg Hempel. This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Germany license.
Download from WikiMedia.org

Additional Filter Result Images

The following series of images represent additional filter results.

Colour Values	Red	Filter Size	9
Edge Tracing	Double Intensity	Edge Threshold	60

Colour Values	Green, Blue	Filter Size	9
Edge Tracing	Double Intensity	Edge Threshold	60

Colour Values	Green, Blue	Filter Size	9
Edge Tracing	Black	Edge Threshold	60

Colour Values	Red, Green, Blue	Filter Size	9
Edge Tracing	Double Intensity	Edge Threshold	55

Colour Values	Red, Green, Blue	Filter Size	11
Edge Tracing	Black	Edge Threshold	75

Colour Values	Red, Blue	Filter Size	11
Edge Tracing	Double Intensity	Edge Threshold	75

Colour Values	Green	Filter Size	17
Edge Tracing	Black	Edge Threshold	85

Colour Values	Red	Filter Size	17
Edge Tracing	Black	Edge Threshold	85

Colour Values	Red, Green, Blue	Filter Size	5
Edge Tracing	Half Edge	Edge Threshold	85

Colour Values	Blue	Filter Size	5
Edge Tracing	Double Intensity	Edge Threshold	75

Colour Values	Green	Filter Size	5
Edge Tracing	Double Intensity	Edge Threshold	75

Colour Values	Red	Filter Size	5
Edge Tracing	Double Intensity	Edge Threshold	75

Colour Values	Red, Green, Blue	Filter Size	5
Edge Tracing	Black	Edge Threshold	75

Colour Values	Red, Green, Blue	Filter Size	9
Edge Tracing	Black	Edge Threshold	55

Colour Values	Red, Green, Blue	Filter Size	3
Edge Tracing	Black	Edge Threshold	75

C# How to: Image Boundary Extraction

Published July 21, 2013 Augmented Reality , Blogging , C# , Code Samples , Edge Detection , Extension Methods , Graphic Filters , Graphics , How to , Image Arithmetic , Image Filters , Image Processing , Image Transform , Learn Everyday , Microsoft , Morphology Filters , New Version , Opensource , Tip , Update 5 Comments
Tags: Augmented Reality, Binary Image, Bitmap ARGB, Bitmap Filters, Bitmap.LockBits, BitmapData, Boundary Sharpen, Boundary Tracing, C#, Code Sample, Computer vision, Converting Images, Edge Detect, Edge Detection, Edge enhance, Edge Tracing, Feature extraction, Graphic Filters, Image, Image Boundary Extraction, Image Difference, Image Dilation, Image Erosion, Image Intensity, Image Morphology, Image processing, Image Sharpen, Image Transform, Machine vision, Morphologial Filters, Morphological Edge Detection, Morphological Transform, Pixel Filters, Structured Element

Article Purpose

This article explores various image processing concepts, which feature in combination when implementing Image Boundary Extraction. Concepts covered within this article include: Morphological Image Erosion and Image Dilation, Image Addition and Subtraction, Boundary Sharpening, Boundary Tracing and Boundary Extraction.

Parrot: Boundary Extraction, 3×3, Red, Green, Blue

Sample Source Code

This article is accompanied by a sample source code Visual Studio project which is available for download here.

Using the Sample Application

This article’s accompanying sample source code includes the definition of a sample application. The sample application serves as an implementation of the concepts discussed in this article. In using the sample application concepts can be easily tested and replicated.

The sample application has been defined as a Windows Forms application. The user interface enables the user to configure several options which influence the output produced from image filtering processes. The following section describes the options available to a user when executing the sample application:

Loading and Saving files – Users can specify source/input images through clicking the Load Image button. If desired, resulting filtered images can be saved to the local file system when clicking the Save Image button.
Filter Type – The types of filters implemented represent variations on Image Boundary Extraction. The supported filter types are: Conventional Boundary extraction, Boundary Sharpening and Boundary Tracing.
Filter Size – Filter intensity/strength will mostly be reliant on the filter size implemented. A Filter size represents the number of neighbouring pixels examined when applying filters.
Colours Applied – The sample source code and sample application provides functionality allowing a filter to only effect user specified colour components. Colour components are represented in the form of an RGB colour scheme. The inclusion or exclusion of the colour components Red, Green and Blue will be determined through user configuration.
Structuring Element – As mentioned, the Filter Size option determines the size of neighbourhood pixels examined. The Structuring Element’s setup determine the neighbouring pixels within the pixel neighbourhood size bounds that should be used as input when calculating filter results.

The following image is a screenshot of the Image Boundary Extraction sample application in action:

Parrot: Boundary Extraction, 3×3, Green

Morphological Boundary Extraction

Image Boundary Extraction can be considered a method of Image Edge Detection. In contrast to more commonly implemented gradient based edge detection methods, Image Boundary Extraction originates from Morphological Image Filters.

When drawing a comparison, Image Boundary Extraction and Morphological Edge Detection express strong similarities. Morphological Edge Detection results from the difference in image erosion and image dilation. Considered from a different point of view, creating one image expressing thicker edges and another image expressing thinner edges provides the means to calculate the difference in edges.

Image Boundary Extraction implements the same concept as Morphological Edge Detection. The base concept can be regarded as calculating the difference between two images which rendered the same image, but expressing a difference in image edges. Image Boundary Extraction relies on calculating the difference between either image erosion and the source image or image dilation and the source image. The difference between image erosion and image dilation in most cases result in more of difference than the difference between image erosion and the source image or image dilation and the source image. The result of Image Boundary Extraction representing less of a difference than Morphological Edge Detection can be observed in Image Boundary Extraction being expressed in finer/smaller width lines.

Difference of Gaussians is another method of edge detection which functions along the same basis. Edges are determined by calculating the difference between two images, each having been filtered from the same source image, using a Gaussian blur of differing intensity levels.

Parrot: Boundary Extraction, 3×3, Red, Green, Blue

Boundary Sharpening

The concept of Boundary Sharpening refers to enhancing or sharpening the boundaries or edges expressed in a source/input image. Boundaries can be easily determined or extracted as discussed earlier when exploring Boundary Extraction.

The steps involved in performing Boundary Sharpening can be described as follows:

Extract Boundaries – Determine boundaries by performing image dilation and calculating the difference between the dilated image and the source image.
Match Source Edges and Extracted Boundaries – The boundaries extracted in the previous step represent the difference between image dilation and the original source image. Ensure that extracted boundaries match the source image through performing image dilation on a copy of the source/input image.
Emphasise Extracted boundaries in source image – Perform image addition using the extracted boundaries image and dilated copy of the source image.

Parrot: Boundary Extraction, 3×3, Red, Green, Blue

Boundary Tracing

Boundary Tracing refers to applying image filters which result in image edges/boundaries appearing darker or more pronounced. This type of filter also relies on Boundary Extraction.

Boundary Tracing can be implemented in two steps, described as follows:

Extract Boundaries – Determine boundaries by performing image dilation and calculating the difference between the dilated image and the source image.
Emphasise Extracted boundaries in source image – Subtract the extracted boundaries from the original source image.

Parrot: Boundary Extraction, 3×3, Red, Green, Blue

Implementing Morphological Erosion and Dilation

The accompanying sample source code defines the MorphologyOperation method, defined as an extension method targeting the Bitmap class. In terms of parameters this method expects a two dimensional array representing a structuring element. The other required parameter represents an enumeration value indicating which Morphological Operation to perform, either erosion or dilation.

The following code snippet provides the definition in full:

private static Bitmap MorphologyOperation(this Bitmap sourceBitmap,
                                          bool[,] se,
                                          MorphologyOperationType morphType,
                                          bool applyBlue = true,
                                          bool applyGreen = true,
                                          bool applyRed = true)
{ 
    BitmapData sourceData =
               sourceBitmap.LockBits(new Rectangle(0, 0,
               sourceBitmap.Width, sourceBitmap.Height),
               ImageLockMode.ReadOnly,
               PixelFormat.Format32bppArgb);


    byte[] pixelBuffer = new byte[sourceData.Stride *
                                  sourceData.Height];


    byte[] resultBuffer = new byte[sourceData.Stride *
                                   sourceData.Height];


    Marshal.Copy(sourceData.Scan0, pixelBuffer, 0,
                               pixelBuffer.Length);


    sourceBitmap.UnlockBits(sourceData);


    int filterOffset = (se.GetLength(0) - 1) / 2;
    int calcOffset = 0, byteOffset = 0;
    byte blueErode = 0, greenErode = 0, redErode = 0;
    byte blueDilate = 0, greenDilate = 0, redDilate = 0;


    for (int offsetY = 0; offsetY <
        sourceBitmap.Height - filterOffset; offsetY++)
    {
        for (int offsetX = 0; offsetX <
            sourceBitmap.Width - filterOffset; offsetX++)
        {
            byteOffset = offsetY * sourceData.Stride +
                         offsetX * 4;


            blueErode = 255; greenErode = 255; redErode = 255;
            blueDilate = 0; greenDilate = 0; redDilate = 0;


            for (int filterY = -filterOffset;
                    filterY <= filterOffset; filterY++)
            {
                for (int filterX = -filterOffset;
                    filterX <= filterOffset; filterX++)
                {
                    if (se[filterY + filterOffset,  
                           filterX + filterOffset] == true)
                    {
                        calcOffset = byteOffset +
                                     (filterX * 4) +
                        (filterY * sourceData.Stride);


                        calcOffset = (calcOffset < 0 ? 0 :
                        (calcOffset >= pixelBuffer.Length + 2 ?
                        pixelBuffer.Length - 3 : calcOffset));


                        blueDilate =  
                        (pixelBuffer[calcOffset] > blueDilate ?
                        pixelBuffer[calcOffset] : blueDilate);


                        greenDilate =  
                        (pixelBuffer[calcOffset + 1] > greenDilate ?
                        pixelBuffer[calcOffset + 1] : greenDilate);


                        redDilate =  
                        (pixelBuffer[calcOffset + 2] > redDilate ?
                        pixelBuffer[calcOffset + 2] : redDilate);


                        blueErode =  
                        (pixelBuffer[calcOffset] < blueErode ?
                        pixelBuffer[calcOffset] : blueErode);


                        greenErode =  
                        (pixelBuffer[calcOffset + 1] < greenErode ?
                        pixelBuffer[calcOffset + 1] : greenErode);


                        redErode =  
                        (pixelBuffer[calcOffset + 2] < redErode ?
                        pixelBuffer[calcOffset + 2] : redErode);
                    }
                }
            }


            blueErode = (applyBlue ? blueErode : pixelBuffer[byteOffset]);
            blueDilate = (applyBlue ? blueDilate : pixelBuffer[byteOffset]);


            greenErode = (applyGreen ? greenErode : pixelBuffer[byteOffset + 1]);
            greenDilate = (applyGreen ? greenDilate : pixelBuffer[byteOffset + 1]);


            redErode = (applyRed ? redErode : pixelBuffer[byteOffset + 2]);
            redDilate = (applyRed ? redDilate : pixelBuffer[byteOffset + 2]);


            if (morphType == MorphologyOperationType.Erosion)
            {
                resultBuffer[byteOffset] = blueErode;
                resultBuffer[byteOffset + 1] = greenErode;
                resultBuffer[byteOffset + 2] = redErode;
            }
            else if (morphType == MorphologyOperationType.Dilation)
            {
                resultBuffer[byteOffset] = blueDilate;
                resultBuffer[byteOffset + 1] = greenDilate;
                resultBuffer[byteOffset + 2] = redDilate;
            }


            resultBuffer[byteOffset + 3] = 255;
        }
    }


    Bitmap resultBitmap = new Bitmap(sourceBitmap.Width, sourceBitmap.Height);
    BitmapData resultData = resultBitmap.LockBits(new Rectangle(0, 0,
                            resultBitmap.Width, resultBitmap.Height),
                            ImageLockMode.WriteOnly,
                            PixelFormat.Format32bppArgb);


    Marshal.Copy(resultBuffer, 0, resultData.Scan0,
                               resultBuffer.Length);


    resultBitmap.UnlockBits(resultData);


    return resultBitmap;
}

Parrot: Boundary Extraction, 3×3, Red, Green

Implementing Image Addition

The sample source code encapsulates the process of combining two separate images through means of addition. The AddImage method serves as a single declaration of image addition functionality. This method has been defined as an extension method targeting the Bitmap class. Boundary Sharpen filtering implements image addition.

The following code snippet provides the definition of the AddImage extension method:

private static Bitmap AddImage(this Bitmapsource Bitmap, 
                               Bitmap addBitmap)
{
    BitmapData sourceData =
               sourceBitmap.LockBits(new Rectangle (0, 0,
               sourceBitmap.Width, sourceBitmap.Height),
               ImageLockMode.ReadOnly,
               PixelFormat.Format32bppArgb);


    byte[] resultBuffer = new byte[sourceData.Stride *
                                  sourceData.Height];


    Marshal.Copy(sourceData.Scan0, resultBuffer, 0,
                               resultBuffer.Length);


    sourceBitmap.UnlockBits(sourceData);


    BitmapData addData =
               addBitmap.LockBits(new Rectangle(0, 0,
               addBitmap.Width, addBitmap.Height),
               ImageLockMode.ReadOnly,
               PixelFormat.Format32bppArgb);


    byte[] addBuffer = new byte[addData.Stride *
                                  addData.Height];


    Marshal.Copy(addData.Scan0, addBuffer, 0,
                               addBuffer.Length);


    addBitmap.UnlockBits(addData);


    for (int k = 0; k + 4 < resultBuffer.Length &&
                    k + 4 < addBuffer.Length; k += 4)
    {
        resultBuffer[k] =
        AddColors(resultBuffer[k], addBuffer[k]);
 
        resultBuffer[k + 1] =
        AddColors(resultBuffer[k + 1], addBuffer[k + 1]);
 
        resultBuffer[k + 2] =
        AddColors(resultBuffer[k + 2], addBuffer[k + 2]);
 
        resultBuffer[k + 3] = 255;
    }


    Bitmap resultBitmap = new Bitmap(sourceBitmap.Width,
                                     sourceBitmap.Height);


    BitmapData resultData =
               resultBitmap.LockBits(new Rectangle(0, 0,
               resultBitmap.Width, resultBitmap.Height),
               ImageLockMode.WriteOnly,
               PixelFormat.Format32bppArgb);


    Marshal.Copy(resultBuffer, 0, resultData.Scan0,
                               resultBuffer.Length);


    resultBitmap.UnlockBits(resultData);


    return resultBitmap;
}

private static byte AddColors(byte color1, byte color2) 
{
    int result = color1 + color2; 

 
    return (byte)(result < 0 ? 0 : (result > 255 ? 255 : result)); 
}

Parrot: Boundary Extraction, 3×3, Red, Green, Blue

Implementing Image Subtraction

In a similar fashion regarding the AddImage method the sample code defines the SubractImage method. By definition this method serves as an extension method targeting the Bitmap class. Image subtraction has been implemented in Boundary Extraction and Boundary Tracing.

The definition of the SubtractImage method listed as follows:

private static Bitmap SubtractImage(this Bitmap sourceBitmap,  
                                         Bitmap subtractBitmap) 
{
    BitmapData sourceData = 
               sourceBitmap.LockBits(new Rectangle(0, 0, 
               sourceBitmap.Width, sourceBitmap.Height), 
               ImageLockMode.ReadOnly, 
               PixelFormat.Format32bppArgb); 


    byte[] resultBuffer = new byte[sourceData.Stride * 
                                  sourceData.Height]; 


    Marshal.Copy(sourceData.Scan0, resultBuffer, 0, 
                               resultBuffer.Length); 


    sourceBitmap.UnlockBits(sourceData); 


    BitmapData subtractData = 
               subtractBitmap.LockBits(new Rectangle(0, 0, 
               subtractBitmap.Width, subtractBitmap.Height), 
               ImageLockMode.ReadOnly, 
               PixelFormat.Format32bppArgb); 


    byte[] subtractBuffer = new byte[subtractData.Stride * 
                                  subtractData.Height]; 


    Marshal.Copy(subtractData.Scan0, subtractBuffer, 0, 
                               subtractBuffer.Length); 


    subtractBitmap.UnlockBits(subtractData); 


    for (int  k = 0; k + 4 < resultBuffer.Length &&  
                    k + 4 < subtractBuffer.Length; k += 4) 
    { 
        resultBuffer[k] =  
        SubtractColors(resultBuffer[k], subtractBuffer[k]); 


        resultBuffer[k + 1] =  
        SubtractColors(resultBuffer[k + 1], subtractBuffer[k + 1]); 


        resultBuffer[k + 2] = 
        SubtractColors(resultBuffer[k + 2], subtractBuffer[k + 2]); 


        resultBuffer[k + 3] = 255; 
    }


    Bitmap resultBitmap = new Bitmap (sourceBitmap.Width, 
                                     sourceBitmap.Height); 


    BitmapData resultData = 
               resultBitmap.LockBits(new Rectangle (0, 0, 
               resultBitmap.Width, resultBitmap.Height), 
               ImageLockMode.WriteOnly, 
               PixelFormat.Format32bppArgb); 


    Marshal.Copy(resultBuffer, 0, resultData.Scan0, 
                               resultBuffer.Length); 


    resultBitmap.UnlockBits(resultData); 


    return resultBitmap; 
}

private static byte SubtractColors(byte color1, byte color2) 
{
    int result = (int)color1 - (int)color2; 

 
    return (byte)(result < 0 ? 0 : result); 
}

Parrot: Boundary Extraction, 3×3, Green

Implementing Image Boundary Extraction

In the sample source code processing Image Boundary Extraction can be achieved when invoking the BoundaryExtraction method. Defined as an extension method, the BoundaryExtraction method targets the Bitmap class.

As discussed earlier, this method performs Boundary Extraction through subtracting the source image from a dilated copy of the source image.

The following code snippet details the definition of the BoundaryExtraction method:

private static Bitmap
BoundaryExtraction(this Bitmap sourceBitmap, 
                   bool[,] se, bool applyBlue = true, 
                   bool applyGreen = true, bool applyRed = true) 
{
    Bitmap resultBitmap = 
           sourceBitmap.MorphologyOperation(se,  
           MorphologyOperationType.Dilation, applyBlue,  
                                  applyGreen, applyRed); 

 
    resultBitmap = resultBitmap.SubtractImage(sourceBitmap); 

  
    return resultBitmap; 
}

Parrot: Boundary Extraction, 3×3, Red, Blue

Implementing Image Boundary Sharpening

Boundary Sharpening in the sample source code has been implemented through the definition of the BoundarySharpen method. The BoundarySharpen extension method targets the Bitmap class. The following code snippet provides the definition:

private static Bitmap 
BoundarySharpen(this Bitmap sourceBitmap, 
                bool[,] se, bool applyBlue = true, 
                bool applyGreen = true, bool applyRed = true) 
{
    Bitmap resultBitmap = 
           sourceBitmap.BoundaryExtraction(se, applyBlue, 
                                           applyGreen, applyRed); 


    resultBitmap = sourceBitmap.MorphologyOperation(se,  
                   MorphologyOperationType.Dilation, applyBlue,  
                   applyGreen, applyRed).AddImage(resultBitmap); 


    return resultBitmap; 
}

Parrot: Boundary Extraction, 3×3, Green

Implementing Image Boundary Tracing

Boundary Tracing has been defined through the BoundaryTrace extension method, which targets the Bitmap class. Similar to the BoundarySharpen method this method performs Boundary Extraction, the result of which serves to be subtracted from the original source image. Subtracting image boundaries/edges result in those boundaries/edges being darkened, or traced. The definition of the BoundaryTracing extension method detailed as follows:

private static Bitmap
BoundaryTrace(this Bitmap sourceBitmap, 
              bool[,] se, bool applyBlue = true, 
              bool applyGreen = true, bool applyRed = true) 
{
    Bitmap resultBitmap =
    sourceBitmap.BoundaryExtraction(se, applyBlue,  
                                    applyGreen, applyRed); 


    resultBitmap = sourceBitmap.SubtractImage(resultBitmap); 

 
    return resultBitmap; 
}

Parrot: Boundary Extraction, 3×3, Green, Blue

Implementing a Wrapper Method

The BoundaryExtractionFilter method is the only method defined as publicly accessible. Following convention, this method’s definition signals the method as an extension method targeting the Bitmap class. This method has the intention of acting as a wrapper method, a single method capable of performing Boundary Extraction, Boundary Sharpening and Boundary Tracing, depending on method parameters.

The definition of the BoundaryExtractionFilter method detailed by the following code snippet:

public static Bitmap
BoundaryExtractionFilter(this Bitmap sourceBitmap, 
                         bool[,] se, BoundaryExtractionFilterType  
                         filterType, bool applyBlue = true, 
                         bool applyGreen = true, bool applyRed = true) 
{
    Bitmap resultBitmap = null; 

 
    if (filterType == BoundaryExtractionFilterType.BoundaryExtraction) 
    {
        resultBitmap =  
        sourceBitmap.BoundaryExtraction(se, applyBlue, 
                                        applyGreen, applyRed); 
    }
    else if (filterType == BoundaryExtractionFilterType.BoundarySharpen) 
    {
        resultBitmap =  
        sourceBitmap.BoundarySharpen(se, applyBlue,  
                                     applyGreen, applyRed); 
    }
    else if (filterType == BoundaryExtractionFilterType.BoundaryTrace) 
    {
        resultBitmap =  
        sourceBitmap.BoundaryTrace(se, applyBlue,  
                                   applyGreen, applyRed); 
    }

 
    return resultBitmap; 
}

Parrot: Boundary Extraction, 3×3, Red, Green, Blue

Sample Images

This article features a number of sample images. All featured images have been licensed allowing for reproduction. The following images feature as sample images:

Scarlet Macaw at Diergaarde Blijdorp, Rotterdam, Netherlands.
Attributed to: Jar0d. This file is licensed under the Creative Commons Attribution-Share Alike 2.0 Generic license.
Download from Wikipedia

A Scarlet Macaw flying away from the photographer.
Attributed to: Robert01. This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Germany license.
Download from Wikipedia

Blue-and-yellow Macaw in flight.
Attributed to: Luc Viatour – www.lucnix.be. This file is licensed under the Creative Commons Attribution 2.0 Generic license.
Download from Wikipedia

Two Green-winged Macaws (also known as the Red-and-green Macaw) resting at Seaport Village, San Diego, USA.
Attributed to: Dave Fayram. This file is licensed under the Creative Commons Attribution 2.0 Generic license.
Download from Wikimedia.org

A Scarlet Macaw riding a small tricycle at an exhibition in Spain.
Attributed to: The Torch. This file is licensed under the Creative Commons Attribution 2.0 Generic license.
Download from Wikipedia.

Long-tailed Broadbill (Psarisomus dalhousiae), Kaeng Krachan National Park, Phetchaburi, Thailand.
Attributed to User-JJ Harrison. This file is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license.
Download from Wikipedia.

C# How to: Weighted Difference of Gaussians

Published July 14, 2013 Augmented Reality , Blogging , C# , Code Samples , Edge Detection , Extension Methods , Graphic Filters , Graphics , How to , Image Arithmetic , Image Convolution , Image Filters , Image Processing , Learn Everyday , Microsoft , New Version , Opensource , Tip , Update 6 Comments
Tags: Augmented Reality, Binary Image, Bitmap ARGB, Bitmap Filters, Bitmap.LockBits, BitmapData, C#, Calculate Matrix, Calculating Kernels, Code Sample, Computer vision, Converting Images, Difference of Gaussians, Edge Detect, Edge Detection, Feature extraction, Gaussian, Gaussian Blur, Gaussian Kernels, Graphic Filters, Image, Image Intensity, Image processing, Image Transform, Machine vision, Pixel Filters

Article Purpose

It is the purpose of this article to illustrate the concept of Difference of Gaussians Edge Detection. This article extends the conventional implementation of Difference of Gaussian algorithms through the application of equally sized matrix kernels only differing by a weight factor.

Frog: Kernel 5×5, Weight1 0.1, Weight2 2.1

Sample Source Code

This article is accompanied by a sample source code Visual Studio project which is available for download here.

Using the Sample Application

This article relies on a sample application included as part of the accompanying sample source code. The sample application serves as a practical implementation of the concepts explored throughout this article.

The sample application user interface enables the user to configure and control the implementation of a Difference of Gaussians Edge Detection Image filter. The configuration options exposed through the sample application’s user interface can be detailed as follows:

Load/Save Images – When executing the sample application users are able to load source/input images from the local file system through clicking the Load Image button. If desired, the sample application enables users to save resulting images to the local file system through clicking the Save Image button.
Kernel Size – This option relates to the size of the matrix kernels that is to be implemented when performing Gaussian Blurring through image convolution. Smaller matrix kernels are faster to compute and generally result in image edges detected in the source/input image to be expressed through thinner gradient edges. Larger matrix kernels can be computationally expensive to compute as kernel sizes increase. In addition, the edges detected in source/input images will generally be expressed as thicker gradient edges in resulting images.
Weight Values – The sample application calculates Gaussian matrix kernels and in doing so implements a weight factor. A Weight Factor determines the blur intensity observed in result images after having applied image convolution. Higher weight factors result in a more intense level of Gaussian Blurring being applied. As expected, lower weight factors values result in a less intense level of Gaussian Blurring being applied. If the value of the first weight factor exceeds the value of the second weight factor resulting images will be generated with a Black background and edges being indicated in White. In a similar fashion, when the second weight factor value exceeds that of the first weight factor resulting images will be generated with a White background and edges being indicated in Black. The greater the difference between the first and second weight factor values result in a greater degree of image noise removal. When weight factor values only differ slightly, resulting images may be prone to image noise.

The following image is screenshot of the Weighted Difference of Gaussians sample application in action:

Frog: Kernel 5×5, Weight1 1.8, Weight2 0.1

Gaussian Blur

The Gaussian Blur algorithm can be described as one of the most popular and widely implemented methods of image blurring. From Wikipedia we gain the following excerpt:

A Gaussian blur (also known as Gaussian smoothing) is the result of blurring an image by a Gaussian function. It is a widely used effect in graphics software, typically to reduce image noise and reduce detail. The visual effect of this blurring technique is a smooth blur resembling that of viewing the image through a translucent screen, distinctly different from the bokeh effect produced by an out-of-focus lens or the shadow of an object under usual illumination. Gaussian smoothing is also used as a pre-processing stage in computer vision algorithms in order to enhance image structures at different scales.

Mathematically, applying a Gaussian blur to an image is the same as convolving the image with a Gaussian function. This is also known as a two-dimensional Weierstrass transform.

Take Note: The Gaussian Blur algorithm has the attribute of smoothing image detail/definition whilst also having an edge preservation attribute. When applying a Gaussian Blur to an image a level of image detail/definition will be blurred/smoothed away, done in a fashion that would exclude/preserve image gradient edges.

Frog: Kernel 5×5, Weight1 2.7, Weight2 0.1

Difference of Gaussians Edge Detection

Difference of Gaussian refers to a specific method of image edge detection. Difference of Gaussians, common abbreviated as DoG, functions through the implementation of Gaussian Image Blurring.

A clear and concise description can be found on the Difference of Gaussians Wikipedia Article Page:

In imaging science, difference of Gaussians is a feature enhancement algorithm that involves the subtraction of one blurred version of an original image from another, less blurred version of the original. In the simple case of grayscale images, the blurred images are obtained by convolving the original grayscale images with Gaussian kernels having differing standard deviations. Blurring an image using a Gaussian kernel suppresses only high-frequency spatial information. Subtracting one image from the other preserves spatial information that lies between the range of frequencies that are preserved in the two blurred images. Thus, the difference of Gaussians is a band-pass filter that discards all but a handful of spatial frequencies that are present in the original grayscale image.

In a conventional sense Difference of Gaussians involves applying Gaussian Blurring to images created as copies of the original source/input image. There must be a difference in the size of the kernels implemented when applying image convolution. A typical example would be applying a 3×3 Gaussian blur on one image copy whilst applying a 5×5 Gaussian blur on another image copy. The final step requires creating a result image populated by subtracting the two blurred image copies. The results obtained from subtraction represents the edges forming part of the source/input image.

This article extends beyond the conventional method of implementing Difference of Gaussians Edge Detection. The implementation illustrated in this article retains the core concept of subtracting values which have been blurred to different intensities. The implementation method explored here differs from the conventional method in the sense that the matrix kernels implemented do not differ in size. Both matrix kernels are in fact required to have the same size dimensions.

The matrix kernels implemented are equal in terms of their size dimensions, although kernel values are different. Expressed from another angle: equally sized matrix kernels of which one represents a more intense level of blurring than the other. A matrix kernels’ resulting intensity can be determined by the weight factor implemented when calculating the kernel values.

Frog: Kernel 5×5, Weight1 3.7, Weight2 0.2

The advantages of implementing equally sized kernels can be described as follows:

Single Convolution implementation: Image Convolution involves executing several nested code loops. Application performance can be severely negatively impacted when executing large nested loops. The conventional method of implementing Difference of Gaussians generally involves having to implement two instances of image convolution, once per image copy. The Difference of Gaussians method implemented in this article executes the code loops related to image convolution only once. Considering the kernels are equal in size, both can be iterated within the same set of loops.

Eliminating Image subtraction: In conventional Difference of Gaussians implementations images expressing differing intensity levels of Gaussian Blurring have to be subtracted. The Difference of Gaussian implementation method described in this article eliminates the need to perform image subtraction. When applying image convolution using both kernels simultaneously the two results obtained, one from each kernel, can be subtracted and assigned to the result image. In addition, through calculating both kernel results at the same time further reduces the need to create two temporary source image copies.

Frog: Kernel 5×5, Weight1 2.4, Weight2 0.3

Difference of Gaussians Edge Detection Required Steps

When implementing a Difference of Gaussians Edge Detection several steps are required, those steps are detailed as follows:

Calculate Kernels – Before implementing image convolution two matrix kernels have to be calculated. The calculated kernels are required to be of equal size and differ in Gaussian Blur intensity. The sample application allows the user to configure kernel intensity through updating the weight values, expressed as Weight 1 and Weight 2.
Convert Source Image to Grayscale – Applying image convolution on grayscale images outperforms convolution on RGB images. When converting an RGB pixel to a grayscale pixel, colour components are combined to form a single gray level intensity. In other words a grayscale image consists of a third of the number of pixels when compared to the RGB image from which the grayscale image had been rendered. In the case of an ARGB image the derived grayscale image will be expressed in 25% of the number of pixels forming part of the source ARGB image. When applying image convolution the number of processor cycles increases when the image pixel count increases.
Perform Convolution Implementing Thresholds – Using the newly created grayscale image perform image convolution for both kernels calculated in the first step. The result value equates to subtracting the two results obtained from convolution. If the result value exceeds the difference between the first and second kernel weight value, the resulting pixel should be set to White, if not, set the result pixel to Black.

Frog: Kernel 5×5, Weight1 2.1, Weight2 0.5

Calculating Gaussian Convolution Kernels

The sample application implements Gaussian Blur Kernel calculations. The matrix kernels implemented in image convolution are calculated at runtime, as opposed to being hard coded. Being able to dynamically construct convolution kernels has the advantage of providing a greater degree of control in runtime regarding image convolution application.

Several steps are involved in calculating Gaussian Blur Matrix Kernels. The first required step being to determine the Matrix Kernel Size and Weight. The size and weight factor of a matrix kernel comprises the two configurable values implemented when calculating Gaussian Blur Kernels. In the case of this article and the sample application those values will be configured by the user through the sample application’s user interface.

The formula implemented in calculating Gaussian Blur Kernels can be expressed as follows:

The formula contains a number of symbols, which define how the filter will be implemented. The symbols forming part of the Gaussian Kernel formula are described in the following list:

G(x y) – A value calculated using the Gaussian Kernel formula. This value forms part of a Kernel, representing a single element.
π – Pi, one of the better known members of the Greek alphabet. The mathematical constant defined as 22 / 7.
σ – The lower case version of the Greek alphabet letter Sigma. This symbol simply represents a threshold or factor value, as specified by the user.
e – The formula references a lower case e symbol. The symbol represents Euler’s number. The value of Euler’s number has been defined as a mathematical constant equating to 2.71828182846.
x, y – The variables referenced as x and y relate to pixel coordinates within an image. y Representing the vertical offset or row and x represents the horizontal offset or column.

Note: The formula’s implementation expects x and y to equal zero values when representing the coordinates of the pixel located in the middle of the kernel.

Frog: Kernel 7×7, Weight1 0.1, Weight2 2.0

Implementing Gaussian Kernel Calculations

The sample application defines the GaussianCalculator.Calculate method. This method accepts two parameters, kernel size and kernel weight. The following code snippet details the implementation:

public static double[,] Calculate(int lenght, double weight) 
{
    double[,] Kernel = new double [lenght, lenght]; 
    double sumTotal = 0; 

  
    int kernelRadius = lenght / 2; 
    double distance = 0; 

  
    double calculatedEuler = 1.0 /  
    (2.0 * Math.PI * Math.Pow(weight, 2)); 

  
    for (int filterY = -kernelRadius; 
         filterY <= kernelRadius; filterY++) 
    {
        for (int filterX = -kernelRadius; 
            filterX <= kernelRadius; filterX++) 
        {
            distance = ((filterX * filterX) +  
                       (filterY * filterY)) /  
                       (2 * (weight * weight)); 

  
            Kernel[filterY + kernelRadius,  
                   filterX + kernelRadius] =  
                   calculatedEuler * Math.Exp(-distance); 

  
            sumTotal += Kernel[filterY + kernelRadius,  
                               filterX + kernelRadius]; 
        } 
    } 

  
    for (int y = 0; y < lenght; y++) 
    { 
        for (int x = 0; x < lenght; x++) 
        { 
            Kernel[y, x] = Kernel[y, x] *  
                           (1.0 / sumTotal); 
        } 
    } 

  
    return Kernel; 
}

Frog: Kernel 3×3, Weight1 0.1, Weight2 1.8

Implementing Difference of Gaussians Edge Detection

The sample source code defines the DifferenceOfGaussianFilter method. This method has been defined as an extension method targeting the Bitmap class. The following code snippet provides the implementation:

public static Bitmap DifferenceOfGaussianFilter(this Bitmap sourceBitmap,  
                                                int matrixSize, double weight1, 
                                                double weight2) 
{
    double[,] kernel1 =  
    GaussianCalculator.Calculate(matrixSize,  
    (weight1 > weight2 ? weight1 : weight2)); 

 
    double[,] kernel2 =  
    GaussianCalculator.Calculate(matrixSize,  
    (weight1 > weight2 ? weight2 : weight1)); 

 
    BitmapData sourceData = sourceBitmap.LockBits(new Rectangle (0, 0, 
                             sourceBitmap.Width, sourceBitmap.Height), 
                                               ImageLockMode.ReadOnly, 
                                         PixelFormat.Format32bppArgb); 

 
    byte[] pixelBuffer = new byte [sourceData.Stride * sourceData.Height]; 
    byte[] resultBuffer = new byte [sourceData.Stride * sourceData.Height]; 
    byte[] grayscaleBuffer = new byte [sourceData.Width * sourceData.Height]; 

 
    Marshal.Copy(sourceData.Scan0, pixelBuffer, 0, pixelBuffer.Length); 
    sourceBitmap.UnlockBits(sourceData); 

 
    double rgb = 0; 

 
    for (int source = 0, dst = 0;  
         source < pixelBuffer.Length && dst < grayscaleBuffer.Length;  
         source += 4, dst++) 
    { 
        rgb = pixelBuffer * 0.11f; 
        rgb += pixelBuffer * 0.59f; 
        rgb += pixelBuffer * 0.3f; 

 
        grayscaleBuffer[dst] = (byte)rgb; 
    }

 
    double color1 = 0.0; 
    double color2 = 0.0; 

 
    int filterOffset = (matrixSize - 1) / 2; 
    int calcOffset = 0; 

 
    for (int source = 0, dst = 0;  
         source < grayscaleBuffer.Length && dst + 4 < resultBuffer.Length;  
         source++, dst += 4) 
    { 
        color1 = 0; 
        color2 = 0; 

         
        for (int filterY = -filterOffset; 
                filterY <= filterOffset; filterY++) 
        {
            for (int filterX = -filterOffset; 
                filterX <= filterOffset; filterX++) 
            {
                calcOffset = source + (filterX) + 
                             (filterY * sourceBitmap.Width); 

 
                calcOffset = (calcOffset < 0 ? 0 :  
                             (calcOffset >= grayscaleBuffer.Length ?  
                             grayscaleBuffer.Length - 1 : calcOffset)); 

 
                color1 += (grayscaleBuffer[calcOffset]) * 
                           kernel1[filterY + filterOffset, 
                           filterX + filterOffset]; 

 
                color2 += (grayscaleBuffer[calcOffset]) * 
                           kernel2[filterY + filterOffset, 
                           filterX + filterOffset]; 
            } 
        }

 
        color1 = color1 - color2; 
        color1 = (color1 >= weight1 - weight2 ? 255 : 0); 

 
        resultBuffer[dst] = (byte)color1; 
        resultBuffer[dst + 1] = (byte)color1; 
        resultBuffer[dst + 2] = (byte)color1; 
        resultBuffer[dst + 3] = 255; 
    }

 
    Bitmap resultBitmap = new Bitmap(sourceBitmap.Width, sourceBitmap.Height); 

 
    BitmapData resultData = resultBitmap.LockBits(new Rectangle (0, 0, 
                             resultBitmap.Width, resultBitmap.Height), 
                                              ImageLockMode.WriteOnly, 
                                         PixelFormat.Format32bppArgb); 

 
    Marshal.Copy(resultBuffer, 0, resultData.Scan0, resultBuffer.Length); 
    resultBitmap.UnlockBits(resultData); 

 
    return resultBitmap; 
}

Frog: Kernel 3×3, Weight1 2.1, Weight2 0.7

Sample Images

This article features a number of sample images. All featured images have been licensed allowing for reproduction. The following image files feature as sample images:

Panamanian Golden Frog – Licensed under the Creative Commons Attribution 2.0 Generic license. Attribution: Brian Gratwicke. Download from Wikipedia.
Dendropsophus Microcephalus – Licensed under the Creative Commons Attribution 2.0 Generic license. Attribution: Brian Gratwicke. Download from Wikipedia.
Tyler’s Tree Frog – Has been released into the public domain by its author, LiquidGhoul. This applies worldwide. Download from Wikipedia.
Mimic Poison Frog – Licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license. Download from Wikipedia.
Phyllobates Terribilis – This file is licensed under the Creative Commons Attribution-Share Alike 2.0 Germany license. Attribution: Wilfried Berns. Download from Wikipedia.

Panamanian Golden Frog

Dendropsophus Microcephalus

Tyler’s Tree Frog

Mimic Poison Frog

Phyllobates Terribilis

C# How to: Difference Of Gaussians

Published May 18, 2013 Augmented Reality , C# , Code Samples , Edge Detection , Extension Methods , Graphic Filters , Graphics , How to , Image Convolution , Image Filters , Microsoft , New Version , Opensource 31 Comments
Tags: Augmented Reality, Bitmap ARGB, Bitmap Filters, Bitmap.LockBits, BitmapData, Blob detection, C#, Change detection, Code Sample, Computer vision, Convolution filters, Convolution Kernel, Convolution matrix, Difference of Gaussians, DoG, DoG Edge Detection, Edge Detect, Edge enhance, Feature extraction, Gaussian, Gaussian Blur, Graphic Filters, Image Noise, Image processing, Image Transform, Machine vision, Matrix, Pixel Filters, Pixel Manipulation, Step detection

Article purpose

In this article we explore the concept of Difference of Gaussians edge detection. This article implements image convolution as a means of achieving Gaussian blurring. All of the concepts explored are implemented by accessing and manipulating the raw pixel data exposed by an image, no GDI+ or conventional drawing code is required.

Sample source code

This article is accompanied by a sample source code Visual Studio project which is available for download here.

Using the Sample Application

The concepts explored in this article can be easily replicated by making use of the Sample Application, which forms part of the associated sample source code accompanying this article.

When using the Difference Of Gaussians sample application you can specify a input/source image by clicking the Load Image button. The dropdown combobox towards the bottom middle part of the screen relates the various edge detection methods discussed.

If desired a user can save the resulting edge detection image to the local file system by clicking the Save Image button.

The following image is screenshot of the Difference Of Gaussians sample application in action:

What is Difference of Gaussians?

Difference of Gaussians, commonly abbreviated as DoG, is a method of implementing image edge detection. Central to the Difference of Gaussians method of edge detection is the application of Gaussian image blur.

From Wikipedia we gain the following explanation:

In imaging science, Difference of Gaussians is a feature enhancement algorithm that involves the subtraction of one blurred version of an original image from another, less blurred version of the original. In the simple case of grayscale images, the blurred images are obtained by convolving the original grayscale images with Gaussian kernels having differing standard deviations. Blurring an image using a Gaussian kernel suppresses only high-frequency spatial information. Subtracting one image from the other preserves spatial information that lies between the range of frequencies that are preserved in the two blurred images. Thus, the difference of Gaussians is a band-pass filter that discards all but a handful of spatial frequencies that are present in the original grayscale image.

In simple terms Difference of Gaussians can be implemented by applying two Gaussian blurs of different intensity levels to the same source image. The resulting image is then created by subtracting the two images of different Gaussian blurring.

Applying a Convolution Matrix filter

In the sample source code accompanying this article Gaussian image blurring is applied by invoking the ConvolutionFilter method. This method accepts a two dimensional array of type double representing the convolution matrix/kernel. This method is also capable of first converting source images to grayscale, which can be specified as a method parameter. Resulting images sometimes tend to be very dark, which can be corrected by specifying a suitable bias value.

The following code snippet provides the implementation of the ConvolutionFilter method:

 private static Bitmap ConvolutionFilter(Bitmap sourceBitmap,  
                                     double[,] filterMatrix,  
                                          double factor = 1,  
                                               int bias = 0,  
                                     bool grayscale = false )  
{ 
     BitmapData sourceData = sourceBitmap.LockBits(new Rectangle (0, 0, 
                              sourceBitmap.Width, sourceBitmap.Height), 
                                                ImageLockMode.ReadOnly,  
                                          PixelFormat.Format32bppArgb); 

  
     byte[] pixelBuffer = new byte[sourceData.Stride * sourceData.Height]; 
     byte[] resultBuffer = new byte[sourceData.Stride * sourceData.Height]; 

  
     Marshal.Copy(sourceData.Scan0, pixelBuffer, 0, pixelBuffer.Length); 
     sourceBitmap.UnlockBits(sourceData); 

  
     if (grayscale == true) 
     {
         float rgb = 0; 

  
         for (int k = 0; k < pixelBuffer.Length; k += 4) 
         { 
             rgb = pixelBuffer[k] * 0.11f; 
             rgb += pixelBuffer[k + 1] * 0.59f; 
             rgb += pixelBuffer[k + 2] * 0.3f; 

  
             pixelBuffer[k] = (byte )rgb; 
             pixelBuffer[k + 1] = pixelBuffer[k]; 
             pixelBuffer[k + 2] = pixelBuffer[k]; 
             pixelBuffer[k + 3] = 255; 
         } 
     } 

  
     double blue = 0.0; 
     double green = 0.0; 
     double red = 0.0; 

  
     int filterWidth = filterMatrix.GetLength(1); 
     int filterHeight = filterMatrix.GetLength(0); 

  
     int filterOffset = (filterWidth-1) / 2; 
     int calcOffset = 0; 

  
     int byteOffset = 0; 

  
     for (int offsetY = filterOffset; offsetY <  
         sourceBitmap.Height - filterOffset; offsetY++) 
     { 
         for (int  offsetX = filterOffset; offsetX <  
             sourceBitmap.Width - filterOffset; offsetX++) 
         { 
             blue = 0; 
             green = 0; 
             red = 0; 

  
             byteOffset = offsetY *  
                          sourceData.Stride +  
                          offsetX * 4; 

   
             for (int  filterY = -filterOffset;  
                 filterY <= filterOffset; filterY++) 
             {
                 for (int  filterX = -filterOffset; 
                     filterX <= filterOffset; filterX++) 
                 { 

   
                     calcOffset = byteOffset +  
                                  (filterX * 4) +  
                                  (filterY * sourceData.Stride); 

  
                     blue += (double)(pixelBuffer[calcOffset]) * 
                             filterMatrix[filterY + filterOffset,  
                                          filterX + filterOffset]; 

  
                     green += (double)(pixelBuffer[calcOffset + 1]) * 
                              filterMatrix[filterY + filterOffset,  
                                           filterX + filterOffset]; 

   
                     red += (double)(pixelBuffer[calcOffset + 2]) * 
                            filterMatrix[filterY + filterOffset,  
                                         filterX + filterOffset]; 
                 }
             }

   
             blue = factor * blue + bias; 
             green = factor * green + bias; 
             red = factor * red + bias; 

  
             if (blue > 255) 
             { blue = 255; } 
             else if (blue < 0) 
             { blue = 0; } 

  
             if (green > 255) 
             { green = 255; } 
             else if (green < 0) 
             { green = 0; } 

  
             if (red > 255) 
             { red = 255; } 
             else if (red < 0) 
             { red = 0; } 

  
             resultBuffer[byteOffset] = (byte )(blue); 
             resultBuffer[byteOffset + 1] = (byte )(green); 
             resultBuffer[byteOffset + 2] = (byte )(red); 
             resultBuffer[byteOffset + 3] = 255; 
        }
    } 

  
     Bitmap resultBitmap = new Bitmap(sourceBitmap.Width,
                                      sourceBitmap.Height); 
  
     BitmapData resultData = resultBitmap.LockBits(new Rectangle (0, 0, 
                              resultBitmap.Width, resultBitmap.Height), 
                                               ImageLockMode .WriteOnly, 
                                          PixelFormat.Format32bppArgb); 

  
     Marshal.Copy(resultBuffer, 0, resultData.Scan0, resultBuffer.Length); 
     resultBitmap.UnlockBits(resultData); 

   
     return resultBitmap; 
}

The Gaussian Matrix

The sample source code defines three Gaussian matrix/kernel values, a 3×3 matrix and two slightly different 5×5 matrices. The Gaussian3x3 matrix requires a factor of 1 / 16, the Gaussian5x5Type1 matrix a factor of 1 / 159 and the factor required by the Gaussian5x5Type2 equates to 1 / 256.

public static class Matrix
{
    public static double[,] Gaussian3x3
    {
        get
        {
            return new double[,]  
            { { 1, 2, 1, }, 
              { 2, 4, 2, },
              { 1, 2, 1, }, };
        }
    }

  
    public static double[,] Gaussian5x5Type1
    {
        get  
        {
            return new double[,]  
             { { 2, 04, 05, 04, 2  }, 
               { 4, 09, 12, 09, 4  }, 
               { 5, 12, 15, 12, 5  },
               { 4, 09, 12, 09, 4  },
               { 2, 04, 05, 04, 2  }, };
        }
    }

  
    public static double[,] Gaussian5x5Type2
    {
        get 
        {
            return new   double[,] 
             { {  1,   4,  6,  4,  1  }, 
               {  4,  16, 24, 16,  4  }, 
               {  6,  24, 36, 24,  6  },
               {  4,  16, 24, 16,  4  },
               {  1,   4,  6,  4,  1  }, };
        }
    }
}

Subtracting Images

When implementing the Difference of Gaussians method of edge detection after having applied two varying levels of Gaussian blurring the resulting images need to be subtracted. The sample source code associated with this article implements the SubtractImage extension method when subtracting images.

The following code snippet details the implementation of the SubtractImage extension method:

private static void SubtractImage(this Bitmap subtractFrom,  
                                  Bitmap subtractValue,  
                                  bool invert = false,
                                  int bias = 0) 
{ 
    BitmapData sourceData = 
               subtractFrom.LockBits(new Rectangle(0, 0, 
               subtractFrom.Width, subtractFrom.Height), 
               ImageLockMode.ReadWrite, 
               PixelFormat.Format32bppArgb); 

   
    byte[] resultBuffer = new byte[sourceData.Stride *
                                  sourceData.Height]; 

  
    Marshal.Copy(sourceData.Scan0, resultBuffer, 0,
                               resultBuffer.Length); 

   
    BitmapData subtractData =
               subtractValue.LockBits(new Rectangle(0, 0, 
               subtractValue.Width, subtractValue.Height), 
               ImageLockMode.ReadOnly, 
               PixelFormat.Format32bppArgb); 

   
    byte[] subtractBuffer = new byte[subtractData.Stride *  
                                     subtractData.Height]; 

   
    Marshal.Copy(subtractData.Scan0, subtractBuffer, 0,  
                                 subtractBuffer.Length); 

   
    subtractValue.UnlockBits(subtractData); 

   
    int blue = 0; 
    int green = 0; 
    int red = 0; 

   
    for (int k = 0; k < resultBuffer.Length &&  
                    k < subtractBuffer.Length; k += 4) 
    { 
        if (invert == true ) 
        { 
            blue = 255 - resultBuffer[k] -  
                   subtractBuffer[k] + bias; 

   
            green = 255 - resultBuffer[k + 1] -  
                    subtractBuffer[k + 1] + bias; 

   
            red = 255 - resultBuffer[k + 2] -  
                  subtractBuffer[k + 2] + bias; 
        } 
        else  
        { 
            blue = resultBuffer[k] -  
                   subtractBuffer[k] + bias; 

   
            green = resultBuffer[k + 1] -  
                    subtractBuffer[k + 1] + bias; 

   
            red = resultBuffer[k + 2] -  
                  subtractBuffer[k + 2] + bias; 
        } 

   
        blue = (blue < 0 ? 0 : (blue > 255 ? 255 : blue)); 
        green = (green < 0 ? 0 : (green > 255 ? 255 : green)); 
        red = (red < 0 ? 0 : (red > 255 ? 255 : red)); 

   
        resultBuffer[k] = (byte )blue; 
        resultBuffer[k + 1] = (byte )green; 
        resultBuffer[k + 2] = (byte )red; 
        resultBuffer[k + 3] = 255; 
    } 

   
    Marshal.Copy(resultBuffer, 0, sourceData.Scan0,  
                               resultBuffer.Length); 

   
    subtractFrom.UnlockBits(sourceData); 
}

Difference of Gaussians Extension methods

The sample source code implements Difference of Gaussians edge detection by means of two extension methods: DifferenceOfGaussians3x5Type1 and DifferenceOfGaussians3x5Type2. Both methods are virtually identical, the only difference being the 5×5 Gaussian matrix being implemented.

Both methods create two new images, each having a Gaussian blur of different levels of intensity applied. The two new images are subtracted in order to create a single resulting image.

The following source code snippet provides the implementation of the DifferenceOfGaussians3x5Type1 and DifferenceOfGaussians3x5Type2 extension methods:

public static Bitmap DifferenceOfGaussians3x5Type1(
                                  this Bitmap sourceBitmap,  
                                  bool grayscale = false, 
                                  bool invert = false, 
                                  int bias = 0) 
{
    Bitmap bitmap3x3 = ExtBitmap.ConvolutionFilter(sourceBitmap, 
                       Matrix.Gaussian3x3, 1.0 / 16.0, 
                       0, grayscale); 

  
    Bitmap bitmap5x5 = ExtBitmap.ConvolutionFilter(sourceBitmap, 
                       Matrix.Gaussian5x5Type1, 1.0 / 159.0,
                       0, grayscale); 

  
    bitmap3x3.SubtractImage(bitmap5x5, invert, bias); 

  
    return bitmap3x3; 
} 

  
public static Bitmap DifferenceOfGaussians3x5Type2(
                                  this Bitmap sourceBitmap,  
                                  bool grayscale = false, 
                                  bool invert = false, 
                                  int bias = 0) 
{ 
    Bitmap bitmap3x3 = ExtBitmap.ConvolutionFilter(sourceBitmap, 
                       Matrix.Gaussian3x3, 1.0 / 16.0, 
                       0, true ); 

  
    Bitmap bitmap5x5 = ExtBitmap.ConvolutionFilter(sourceBitmap, 
                       Matrix.Gaussian5x5Type2, 1.0 / 256.0,
                       0, true ); 

 
    bitmap3x3.SubtractImage(bitmap5x5, invert, bias); 

  
    return bitmap3x3; 
}

Sample Images

The original sample image used in this article is licensed under the Creative Commons Attribution-Share Alike 2.0 Generic license. The original author is attributed as Andrew Dunn – http://www.andrewdunnphoto.com/

The Original Image

Difference Of Gaussians 3×5 Type1

Difference Of Gaussians 3×5 Type2

Difference Of Gaussians 3×5 Type1 Bias 128

Difference Of Gaussians 3×5 Type 2 Bias 96

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