University of Worcester Worcester Research and Publications

Fast Unsupervised Edge Detection Using Genetic Programming Application Notes

Fu, W., Xu, B., Zhang, M. and Johnston, Mark (2018) Fast Unsupervised Edge Detection Using Genetic Programming Application Notes. IEEE Computational Intelligence Magazine, 13 (4). pp. 46-58. ISSN Print: 1556-603X Online: 155-6048

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Edge detection has been a fundamental and important task in computer vision for many years, but it is still a challenging problem in real-time applications, especially for unsupervised edge detection, where ground truth is not available. Typical fast edge detection approaches, such as the single threshold method, are expensive to achieve in unsupervised edge detection. This study proposes a Genetic Programming (GP) based algorithm to quickly and automatically extract binary edges in an unsupervised manner. We investigate how GP can effectively evolve an edge detector from a single image without ground truth, and whether the evolved edge detector can be directly applied to other unseen/test images. The proposed method is examined and compared with a recent GP method and the Canny method on the Berkeley segmentation dataset. The results show that the proposed GP method has the ability to effectively evolve edge detectors by using only a single image as the whole training set, and significantly outperforms the two methods it is compared to. Furthermore, the binary edges detected by the evolved edge detectors have a good balance between recall and precision.

Item Type: Article
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The full-text can be accessed via the Official URL. This article is freely available from IEEE.

Uncontrolled Discrete Keywords: computer vision, edge detection, feature extraction, genetic algorithms, image segmentation, fast unsupervised edge detection, single threshold method, Berkeley segmentation dataset, Canny method, automatic binary edge extraction, computer vision, GP method, genetic programming based algorithm, edge detector, Image edge detection, Feature extraction, Genetic programming, Computational efficiency
Subjects: T Technology > T Technology (General)
Divisions: College of Health, Life and Environmental Sciences > School of Science and the Environment
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Depositing User: Mark Johnston
Date Deposited: 20 Nov 2018 06:54
Last Modified: 17 Jun 2020 17:25

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