Edges are very important to any vision system (biological or machine).
- They are fairly cheap to compute
- They do provide strong visual clues that can help the recognition process.
- Edges are affected by noise present in an image though.
An edge may be regarded as a boundary between two dissimilar regions in an image. These may be different surfaces of the object, or perhaps a boundary between light and shadow falling on a single surface. In principle an edge is easy to find since differences in pixel values between regions are relatively easy to calculate by considering gradients. The goal of edge detection is to mark the points in an image at which the intensity changes sharply. Sharp changes in image properties usually reflect important events and changes in world properties.
Taking an edge to be a change in intensity taking place over a number of pixels, edge detection algorithms generally calculate a derivative of this intensity change.
In image processing , the Sobel operator is a discrete differentiation operator solving for the 1st derivatives of the intensity information in simple edge detection algorithms. In 2D, the operator uses two 3x3 kernels convolved with the original image to produce a map of intensity Gradient. The areas of highest gradient are where the intensity of the image changes rapidly over a few pixels, and are thus likely to represent edges.
Edges are detected in areas of the image where the intensity level fluctuates sharply, the more rapid the intensity changes the stronger the edge. A good edge detection stage makes the formation of extended boundaries and object recognition easier; errors due to a poor edge detector soon become magnified as more processing is performed, so care must be taken in choosing the right edge detector (or operator) for the job.
original image
After Kayyali edge detection
Kayyali (SENW) South Easy North West
6 0 -6
0 0 0
-6 0 6
Kayyali (NESW) North East South West
-6 0 6
0 0 0
6 0 -6
Original image
Kayyali (senw) Kayyali (nesw)
Bravenet.com