{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T21:28:41Z","timestamp":1764797321117,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T00:00:00Z","timestamp":1600992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Conventional image entropy merely involves the overall pixel intensity statistics which cannot respond to intensity patterns over spatial domain. However, spatial distribution of pixel intensity is definitely crucial to any biological or computer vision system, and that is why gestalt grouping rules involve using features of both aspects. Recently, the increasing integration of knowledge from gestalt research into visualization-related techniques has fundamentally altered both fields, offering not only new research questions, but also new ways of solving existing issues. This paper presents a Bayesian edge detector called GestEdge, which is effective in detecting gestalt edges, especially useful for forming object boundaries as perceived by human eyes. GestEdge is characterized by employing a directivity-aware sampling window or mask that iteratively deforms to probe or explore the existence of principal direction of sampling pixels; when convergence is reached, the window covers pixels best representing the directivity in compliance with the similarity and proximity laws in gestalt theory. During the iterative process based on the unsupervised Expectation-Minimization (EM) algorithm, the shape of the sampling window is optimally adjusted. Such a deformable window allows us to exploit the similarity and proximity among the sampled pixels. Comparisons between GestEdge and other edge detectors are shown to justify the effectiveness of GestEdge in extracting the gestalt edges.<\/jats:p>","DOI":"10.3390\/e22101080","type":"journal-article","created":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T08:57:32Z","timestamp":1601024252000},"page":"1080","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Bayesian Edge Detector Using Deformable Directivity-Aware Sampling Window"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2642-8788","authenticated-orcid":false,"given":"Ren-Jie","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, National Taiwan Ocean University, Keelung City 20224, Taiwan"}]},{"given":"Jung-Hua","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Taiwan Ocean University, Keelung City 20224, Taiwan"},{"name":"Deputy, AI Research Center, National Taiwan Ocean University, Keelung City 20224, Taiwan"}]},{"given":"Chun-Shun","family":"Tseng","sequence":"additional","affiliation":[{"name":"Ship and Ocean Industries R&amp;D Center (SOIC), New Taipei City 25170, Taiwan"}]},{"given":"Zhe-Wei","family":"Tu","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Taiwan Ocean University, Keelung City 20224, Taiwan"}]},{"given":"Kai-Chun","family":"Chiang","sequence":"additional","affiliation":[{"name":"Department of Fine Arts, Taipei National University of the Arts, Taipei City 11201, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1049\/iet-ipr.2012.0690","article-title":"Surface fitting for individual image thresholding and beyond","volume":"7","author":"Cai","year":"2013","journal-title":"IET Image Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1270","DOI":"10.1109\/TPAMI.2007.70772","article-title":"Multiscale Categorical Object Recognition Using Contour Fragments","volume":"30","author":"Shotton","year":"2008","journal-title":"IEEE Trans. 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