{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:24:52Z","timestamp":1760243092323,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2015,7,20]],"date-time":"2015-07-20T00:00:00Z","timestamp":1437350400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The role of symmetry in computer vision has waxed and waned in importance during the evolution of the field from its earliest days. At first figuring prominently in support of bottom-up indexing, it fell out of favour as shape gave way to appearance and recognition gave way to detection. With a strong prior in the form of a target object, the role of the weaker priors offered by perceptual grouping was greatly diminished. However, as the field returns to the problem of recognition from a large database, the bottom-up recovery of the parts that make up the objects in a cluttered scene is critical for their recognition. The medial axis community has long exploited the ubiquitous regularity of symmetry as a basis for the decomposition of a closed contour into medial parts. However, today\u2019s recognition systems are faced with cluttered scenes and the assumption that a closed contour exists, i.e., that figure-ground segmentation has been solved, rendering much of the medial axis community\u2019s work inapplicable. In this article, we review a computational framework, previously reported in [1\u20133], that bridges the representation power of the medial axis and the need to recover and group an object\u2019s parts in a cluttered scene. Our framework is rooted in the idea that a maximally-inscribed disc, the building block of a medial axis, can be modelled as a compact superpixel in the image. We evaluate the method on images of cluttered scenes.<\/jats:p>","DOI":"10.3390\/sym7031333","type":"journal-article","created":{"date-parts":[[2015,7,20]],"date-time":"2015-07-20T09:56:54Z","timestamp":1437386214000},"page":"1333-1351","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Framework for Symmetric Part Detection in Cluttered Scenes"],"prefix":"10.3390","volume":"7","author":[{"given":"Tom","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Toronto, 27 King's College Cir, Toronto, Ontario M5S 2J7, Canada"}]},{"given":"Sanja","family":"Fidler","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Toronto, 27 King's College Cir, Toronto, Ontario M5S 2J7, Canada"}]},{"given":"Alex","family":"Levinshtein","sequence":"additional","affiliation":[{"name":"Epson, 185 Renfrew Dr, Markham, Ontario L3R 6G3, Canada"}]},{"given":"Cristian","family":"Sminchisescu","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Engineering, Lund University, 221 00 Lund, Sweden"},{"name":"Institute of Mathematics of the Romanian Academy, Calea Victoriei, 125, Sector 1, 010702 Bucharest, Romania"}]},{"given":"Sven","family":"Dickinson","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Toronto, 27 King's College Cir, Toronto, Ontario M5S 2J7, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2015,7,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lee, T., Fidler, S., and Dickinson, S. 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