A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
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IEEE Transactions on Pattern Analysis and Machine Intelligence
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
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CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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EURASIP Journal on Applied Signal Processing
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GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
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GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
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ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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Image and Vision Computing
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Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Basic features for edge detection, such as derivatives, can be further manipulated to improve detection performance. However, how to effectively combine different basic features remains an open issue and needs to be investigated. In this study, Genetic Programming (GP) is used to automatically and effectively construct rotation variant features based on basic features from derivatives, F-test, and histograms of images. To reduce computational cost in the training stage, the basic features only use the horizontal responses to construct new horizontal features. These new features are then combined with their own rotated versions in the vertical direction in the testing stage. The experimental results show that the rotation variant features constructed by GP combine advantages from the basic features, reduce drawbacks from basic features alone, and improve the detection performance.