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  • Title: Low-level feature extraction for edge detection using genetic programming.
    Author: Fu W, Johnston M, Zhang M.
    Journal: IEEE Trans Cybern; 2014 Aug; 44(8):1459-72. PubMed ID: 24184792.
    Abstract:
    Edge detection is a subjective task. Traditionally, a moving window approach is used, but the window size in edge detection is a tradeoff between localization accuracy and noise rejection. An automatic technique for searching a discriminated pixel's neighbors to construct new edge detectors is appealing to satisfy different tasks. In this paper, we propose a genetic programming (GP) system to automatically search pixels (a discriminated pixel and its neighbors) to construct new low-level subjective edge detectors for detecting edges in natural images, and analyze the pixels selected by the GP edge detectors. Automatically searching pixels avoids the problem of blurring edges from a large window and noise influence from a small window. Linear and second-order filters are constructed from the pixels with high occurrences in these GP edge detectors. The experiment results show that the proposed GP system has good performance. A comparison between the filters with the pixels selected by GP and all pixels in a fixed window indicates that the set of pixels selected by GP is compact but sufficiently rich to construct good edge detectors.
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