Orientation contrast model for boundary detection |
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| Affiliation: | 1. Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China;2. Key Lab of Machine Learning and Computational Intelligence, College of Mathematics and Computer Science, Hebei University, Hebei, China;1. Department of Electrical and Computer Engineering, Concordia University, Montréal, QC H3G 2W1, Canada;2. Concordia Institute for Information Systems Engineering, Concordia University, Montréal, QC H3G 2W1, Canada;1. Department of Electronic Engineering, The Chinese University of Hong Kong, Shatin N.T., Hong Kong;2. Hong Kong Applied Science and Technology Research Institute (ASTRI), Shatin N.T., Hong Kong;1. School of Computer Science, Northwestern Polytechnic University, Xi’an, Shaanxi 710129, PR China;2. School of Electronics and Information, Zhongyuan University of Technology, Zhengzhou, Henan 450007, PR China;3. Department of Computer Science and Information Systems, Birkbeck College, University of London, Bloomsbury, London WC1E 7HX, UK;4. Information Center of Yellow River Conservancy Commission, Zhengzhou, Henan 450003, PR China |
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| Abstract: | The boundary detection task has been extensively studied in the field of computer vision and pattern recognition. Recently, researchers have formulated this task as supervised or unsupervised learning problems to leverage machine learning methods to improve detection accuracy. However, texture suppression, which is important for boundary detection, is not incorporated in this framework. To address this limitation, and also motivated by psychophysical and neurophysiological findings, we propose an orientation contrast model for boundary detection, which combines machine learning technique and texture suppression in a unified framework. Thus, the model is especially suited for detecting object boundaries surrounded by natural textures. Extensive experiments on several benchmarks demonstrate the improved boundary detection performance of the model. Specifically, its detection accuracy was improved by 10% on the Rug dataset compared with state-of-the-art unsupervised boundary detection algorithm, and its performance is also better or at least comparable with previous supervised boundary detection algorithms. |
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| Keywords: | Orientation contrast Boundary detection Edge detection Edge magnitude Edge density Suppression magnitude Edge smoothness Steerable filter |
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