{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:10:35Z","timestamp":1753884635889,"version":"3.41.2"},"reference-count":28,"publisher":"World Scientific Pub Co Pte Ltd","issue":"02","funder":[{"name":"Science and Technology Planning of Shenzhen","award":["JCYJ20180503182133411"],"award-info":[{"award-number":["JCYJ20180503182133411"]}]},{"DOI":"10.13039\/501100018555","name":"Science and Technology Plan Project of Guizhou Province","doi-asserted-by":"crossref","award":["Qiankehe Foundation-ZK[2022] General 550"],"award-info":[{"award-number":["Qiankehe Foundation-ZK[2022] General 550"]}],"id":[{"id":"10.13039\/501100018555","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2023,2]]},"abstract":"<jats:p> Occlusion area detection is a crucial step affecting the performance of the binocular stereo matching algorithm, but the traditional method of occlusion area detection has two major problems, including left\u2013right consistency detection (LRC). First, these algorithms must obtain the left and right disparity maps with precision. Second, these algorithms cannot detect the occlusion region at the image\u2019s borders. We propose the single view occlusion area detective (SVOAD) algorithm to detect these occlusion areas and better deal with them. The SVOAD can detect the area of occlusion from a single image, thereby reducing the computational cost. Additionally, the algorithm can detect the occlusion region in all image regions. This paper also improves the guided filter so that it works better with the end-to-end neural network and makes the SVOAD algorithm work better. <\/jats:p>","DOI":"10.1142\/s0218001423500039","type":"journal-article","created":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T14:55:21Z","timestamp":1671202521000},"source":"Crossref","is-referenced-by-count":3,"title":["An Algorithm for Single View Occlusion Area Detection in Binocular Stereo Matching"],"prefix":"10.1142","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5524-3817","authenticated-orcid":false,"given":"Ren","family":"Qian","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Guizhou University, Guizhou 550025, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Electronic and Computer Engineering, Shenzhen Graduate School of Peking University, Lishui, Nansha, Shenzhen, Guangdong 518055, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renyan","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Guizhou University, Guiyang, Guizhou 550025, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenbang","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Guizhou University, Guizhou 550025, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zaijun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Qiannan Normal University for Nationalities, Duyun, Guizhou 558000, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2023,2,3]]},"reference":[{"issue":"3","key":"S0218001423500039BIB001","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1037\/0033-295X.101.3.414","volume":"101","author":"Anderson B. 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