- DIP - Computer Vision and Graphics
- DIP - Optical Character Recognition
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- DIP - Introduction to Color Spaces
- DIP - High Pass vs Low Pass Filters
- DIP - Convolution theorm
- DIP - Fourier series and Transform
- DIP - Frequency Domain Analysis
- DIP - Laplacian Operator
- DIP - Krisch Compass Mask
- DIP - Robinson Compass Mask
- DIP - Sobel operator
- DIP - Prewitt Operator
- DIP - Concept of Edge Detection
- DIP - Concept of Blurring
- DIP - Concept of Masks
- DIP - Concept of convolution
- DIP - Gray Level Transformations
- DIP - Histogram Equalization
- DIP - Introduction to Probability
- DIP - Histogram Stretching
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- DIP - Image Transformations
- DIP - Brightness and Contrast
- DIP - Histograms Introduction
- DIP - Concept of Dithering
- DIP - ISO Preference curves
- DIP - Concept of Quantization
- DIP - Gray Level Resolution
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- DIP - Grayscale to RGB Conversion
- DIP - Color Codes Conversion
- DIP - Types of Images
- DIP - Concept of Bits Per Pixel
- DIP - Perspective Transformation
- DIP - Concept of Pixel
- DIP - Camera Mechanism
- DIP - Image Formation on Camera
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- DIP - Applications and Usage
- DIP - History of Photography
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- DIP - Image Processing Introduction
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Robinson Compass Mask
Robinson compass masks are another type of derrivate mask which is used for edge detection. This operator is also known as direction mask. In this operator we take one mask and rotate it in all the 8 compass major directions that are following:
North
North West
West
South West
South
South East
East
North East
There is no fixed mask. You can take any mask and you have to rotate it to find edges in all the above mentioned directions. All the masks are rotated on the bases of direction of zero columns.
For example let’s see the following mask which is in North Direction and then rotate it to make all the direction masks.
North Direction Mask
-1 | 0 | 1 |
-2 | 0 | 2 |
-1 | 0 | 1 |
North West Direction Mask
0 | 1 | 2 |
-1 | 0 | 1 |
-2 | -1 | 0 |
West Direction Mask
1 | 2 | 1 |
0 | 0 | 0 |
-1 | -2 | -1 |
South West Direction Mask
2 | 1 | 0 |
1 | 0 | -1 |
0 | -1 | -2 |
South Direction Mask
1 | 0 | -1 |
2 | 0 | -2 |
1 | 0 | -1 |
South East Direction Mask
0 | -1 | -2 |
1 | 0 | -1 |
2 | 1 | 0 |
East Direction Mask
-1 | -2 | -1 |
0 | 0 | 0 |
1 | 2 | 1 |
North East Direction Mask
-2 | -1 | 0 |
-1 | 0 | 1 |
0 | 1 | 2 |
As you can see that all the directions are covered on the basis of zeros direction. Each mask will give you the edges on its direction. Now let’s see the result of the entire above masks. Suppose we have a sample picture from which we have to find all the edges. Here is our sample picture:
Sample Picture
![Robinson Compass Mask](/dip/images/robinson1.jpg)
Now we will apply all the above filters on this image and we get the following result.
North Direction Edges
![Robinson Compass Mask](/dip/images/robinson2.jpg)
North West Direction Edges
![Robinson Compass Mask](/dip/images/robinson3.jpg)
West Direction Edges
![Robinson Compass Mask](/dip/images/robinson4.jpg)
South West Direction Edges
![Robinson Compass Mask](/dip/images/robinson5.jpg)
South Direction Edges
![Robinson Compass Mask](/dip/images/robinson6.jpg)
South East Direction Edges
![Robinson Compass Mask](/dip/images/robinson7.jpg)
East Direction Edges
![Robinson Compass Mask](/dip/images/robinson8.jpg)
North East Direction Edges
![Robinson Compass Mask](/dip/images/robinson9.jpg)
As you can see that by applying all the above masks you will get edges in all the direction. Result is also depends on the image. Suppose there is an image, which do not have any North East direction edges so then that mask will be ineffective.
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