Contour Integration using Graph-Cut and Non-Classical Receptive Field

• URL: http://arxiv.org/abs/2010.14561v2
• Date: Mon, 10 May 2021 21:07:53 GMT
• Title: Contour Integration using Graph-Cut and Non-Classical Receptive Field
• Authors: Zahra Mousavi Kouzehkanan, Reshad Hosseini, Babak Nadjar Araabi
• Abstract summary: We propose a novel method to detect image contours from the extracted edge segments of other algorithms. The proposed energy functions are inspired by the surround modulation in the primary visual cortex that help suppressing texture noise.
• Score: 4.935491924643742
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Many edge and contour detection algorithms give a soft-value as an output and the final binary map is commonly obtained by applying an optimal threshold. In this paper, we propose a novel method to detect image contours from the extracted edge segments of other algorithms. Our method is based on an undirected graphical model with the edge segments set as the vertices. The proposed energy functions are inspired by the surround modulation in the primary visual cortex that help suppressing texture noise. Our algorithm can improve extracting the binary map, because it considers other important factors such as connectivity, smoothness, and length of the contour beside the soft-values. Our quantitative and qualitative experimental results show the efficacy of the proposed method.

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