Genetic Programming for Edge Detection Using Blocks to Extract Features

Single pixels can be directly used to construct low-level edge detectors but these detectors are not good for suppressing noise and some texture. In general, features based on a small area are used to suppress noise and texture. However, there is very little guidance in the literature on how to select the area size. In this paper, we employ Genetic Programming (GP) to evolve edge detectors via automatically searching for features based on flexible blocks rather than dividing a fixed window into small areas based on different directions. Experimental results for natural images show that using blocks to extract features obtains better performance than using single pixels only to construct detectors, and that GP can successfully choose the block size for extracting features.