2015
DOI: 10.1167/15.9.20
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Computational modeling of depth ordering in occlusion through accretion or deletion of texture

Abstract: Understanding the depth ordering of surfaces in the natural world is one of the most fundamental operations of the primate visual system. Surfaces that undergo accretion or deletion (AD) of texture are always perceived to behind an adjacent surface. An updated ForMotionOcclusion model (Barnes & Mingolla, 2013) includes two streams for computing motion signals and boundary signals. The two streams generate depth percepts such that AD signals together with boundary signals generate a farther depth on the occlude… Show more

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Cited by 3 publications
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“…It can resolve ambiguities seen in bistable motion displays, such as direction of rotation ambiguity created when viewing parallel projection of an object rotating in depth (Braunstein, Andersen, & Riefer, 1982), or the ambiguity in the overall structure from point-light biological motion displays (Profitt et al, 1984). As a result, the assumption that accreting/deleting surfaces are invariably interpreted as behind began to appear in textbooks (Goldstein, 2014;Howard & Rogers, 2002;Niyogi, 1995) and has been incorporated into computational models of depth from motion Thompson, Mutch, & Berzins, 1985;Berzhanskaya, Grossberg, & Mingolla, 2007;Beck, Ognibeni, & Neumann, 2008;Raudies & Neumann, 2010; Barnes & Mingolla, 2013;Layton & Yazdanbakhsh, 2015;Ruda, Livitz, Riesen, & Mingolla, 2015).…”
mentioning
confidence: 99%
“…It can resolve ambiguities seen in bistable motion displays, such as direction of rotation ambiguity created when viewing parallel projection of an object rotating in depth (Braunstein, Andersen, & Riefer, 1982), or the ambiguity in the overall structure from point-light biological motion displays (Profitt et al, 1984). As a result, the assumption that accreting/deleting surfaces are invariably interpreted as behind began to appear in textbooks (Goldstein, 2014;Howard & Rogers, 2002;Niyogi, 1995) and has been incorporated into computational models of depth from motion Thompson, Mutch, & Berzins, 1985;Berzhanskaya, Grossberg, & Mingolla, 2007;Beck, Ognibeni, & Neumann, 2008;Raudies & Neumann, 2010; Barnes & Mingolla, 2013;Layton & Yazdanbakhsh, 2015;Ruda, Livitz, Riesen, & Mingolla, 2015).…”
mentioning
confidence: 99%