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Low-Level Feature Extraction for Edge Detection Using Genetic Programming.
- Source :
- IEEE Transactions on Cybernetics; Aug2014, Vol. 44 Issue 8, p1459-1472, 14p
- Publication Year :
- 2014
-
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. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 21682267
- Volume :
- 44
- Issue :
- 8
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Cybernetics
- Publication Type :
- Academic Journal
- Accession number :
- 97129531
- Full Text :
- https://doi.org/10.1109/TCYB.2013.2286611