
- Digital Image Processing
- DIP - Home
- DIP - Image Processing Introduction
- DIP - Signal and System Introduction
- DIP - History of Photography
- DIP - Applications and Usage
- DIP - Concept of Dimensions
- DIP - Image Formation on Camera
- DIP - Camera Mechanism
- DIP - Concept of Pixel
- DIP - Perspective Transformation
- DIP - Concept of Bits Per Pixel
- DIP - Types of Images
- DIP - Color Codes Conversion
- DIP - Grayscale to RGB Conversion
- DIP - Concept of Sampling
- DIP - Pixel Resolution
- DIP - Concept of Zooming
- DIP - Zooming methods
- DIP - Spatial Resolution
- DIP - Pixels Dots and Lines per inch
- DIP - Gray Level Resolution
- DIP - Concept of Quantization
- DIP - ISO Preference curves
- DIP - Concept of Dithering
- DIP - Histograms Introduction
- DIP - Brightness and Contrast
- DIP - Image Transformations
- DIP - Histogram Sliding
- DIP - Histogram Stretching
- DIP - Introduction to Probability
- DIP - Histogram Equalization
- DIP - Gray Level Transformations
- DIP - Concept of convolution
- DIP - Concept of Masks
- DIP - Concept of Blurring
- DIP - Concept of Edge Detection
- DIP - Prewitt Operator
- DIP - Sobel operator
- DIP - Robinson Compass Mask
- DIP - Krisch Compass Mask
- DIP - Laplacian Operator
- DIP - Frequency Domain Analysis
- DIP - Fourier series and Transform
- DIP - Convolution theorm
- DIP - High Pass vs Low Pass Filters
- DIP - Introduction to Color Spaces
- DIP - JPEG compression
- DIP - Optical Character Recognition
- DIP - Computer Vision and Graphics
- DIP Useful Resources
- DIP - Quick Guide
- DIP - Useful Resources
- DIP - Discussion
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:

Now we will apply all the above filters on this image and we get the following result.
North Direction Edges

North West Direction Edges

West Direction Edges

South West Direction Edges

South Direction Edges

South East Direction Edges

East Direction Edges

North East Direction Edges

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.