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      A Fast Contour Detection Model Inspired by Biological Mechanisms in Primary Vision System

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          Abstract

          Compared to computer vision systems, the human visual system is more fast and accurate. It is well accepted that V1 neurons can well encode contour information. There are plenty of computational models about contour detection based on the mechanism of the V1 neurons. Multiple-cue inhibition operator is one well-known model, which is based on the mechanism of V1 neurons' non-classical receptive fields. However, this model is time-consuming and noisy. To solve these two problems, we propose an improved model which integrates some additional other mechanisms of the primary vision system. Firstly, based on the knowledge that the salient contours only occupy a small portion of the whole image, the prior filtering is introduced to decrease the running time. Secondly, based on the physiological finding that nearby neurons often have highly correlated responses and thus include redundant information, we adopt the uniform samplings to speed up the algorithm. Thirdly, sparse coding is introduced to suppress the unwanted noises. Finally, to validate the performance, we test it on Berkeley Segmentation Data Set. The results show that the improved model can decrease running time as well as keep the accuracy of the contour detection.

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          Most cited references26

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          Receptive fields of single neurones in the cat's striate cortex.

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            Learning to detect natural image boundaries using local brightness, color, and texture cues.

            The goal of this work is to accurately detect and localize boundaries in natural scenes using local image measurements. We formulate features that respond to characteristic changes in brightness, color, and texture associated with natural boundaries. In order to combine the information from these features in an optimal way, we train a classifier using human labeled images as ground truth. The output of this classifier provides the posterior probability of a boundary at each image location and orientation. We present precision-recall curves showing that the resulting detector significantly outperforms existing approaches. Our two main results are 1) that cue combination can be performed adequately with a simple linear model and 2) that a proper, explicit treatment of texture is required to detect boundaries in natural images.
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              A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

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                Author and article information

                Contributors
                Journal
                Front Comput Neurosci
                Front Comput Neurosci
                Front. Comput. Neurosci.
                Frontiers in Computational Neuroscience
                Frontiers Media S.A.
                1662-5188
                30 April 2018
                2018
                : 12
                : 28
                Affiliations
                [1] 1Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences , Beijing, China
                [2] 2University of Chinese Academy of Sciences , Beijing, China
                [3] 3Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Science , Shanghai, China
                [4] 4National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences , Beijing, China
                Author notes

                Edited by: Si Wu, Peking University, China

                Reviewed by: Teresa Serrano-Gotarredona, Consejo Superior de Investigaciones Científicas (CSIC), Spain; Bing Zhou, Sam Houston State University, United States

                *Correspondence: Yi Zeng yi.zeng@ 123456ia.ac.cn

                †These authors have contributed equally to this work and co-first authors.

                Article
                10.3389/fncom.2018.00028
                5936787
                df510967-69ab-4974-bf10-4a41647a8b65
                Copyright © 2018 Kang, Kong, Zeng and Xu.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 20 October 2017
                : 10 April 2018
                Page count
                Figures: 8, Tables: 5, Equations: 17, References: 29, Pages: 9, Words: 4748
                Categories
                Neuroscience
                Methods

                Neurosciences
                primary visual system,biological mechanism,contour detection,prior filtering,uniform sampling,sparse coding

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