000172707 001__ 172707
000172707 005__ 20181203022602.0
000172707 0247_ $$2doi$$a10.1037/a0019076
000172707 02470 $$2ISI$$a000276928600004
000172707 037__ $$aARTICLE
000172707 245__ $$aSurface Construction by a 2-D Differentiation-Integration Process: A Neurocomputational Model for Perceived Border Ownership, Depth, and Lightness in Kanizsa Figures
000172707 269__ $$a2010
000172707 260__ $$c2010
000172707 336__ $$aJournal Articles
000172707 520__ $$aHuman visual perception is a fundamentally relational process: Lightness perception depends on luminance ratios, and depth perception depends on occlusion (difference of depth) cues. Neurons in low-level visual cortex are sensitive to the difference (but not the value itself) of signals, and these differences have to be used to reconstruct the input. This process can be regarded as a 2-dimensional differentiation and integration process: First, differentiated signals for depth and lightness are created at an earlier stage of visual processing and then 2-dimensionally integrated at a later stage to construct surfaces. The subjective filling in of physically missing parts of input images (completion) can be explained as a property that emerges from this surface construction process. This approach is implemented in a computational model, called DISC (Differentiation-Integration for Surface Completion). In the DISC model, border ownership (the depth order at borderlines) is computed based on local occlusion cues (L- and T-junctions) and the distribution of borderlines. Two-dimensional integration is then applied to construct surfaces in the depth domain, and lightness values are in turn modified by these depth measurements. Illusory percepts emerge through the surface-construction process with the development of illusory border ownership and through the interaction between depth and lightness perception. The DISC model not only produces a central surface with the correctly modified lightness values of the original Kanizsa figure but also responds to variations of this figure such that it can distinguish between illusory and nonillusory configurations in a manner that is consistent with human perception.
000172707 6531_ $$aillusory contours
000172707 6531_ $$asurface completion
000172707 6531_ $$adepth perception
000172707 6531_ $$alightness/brightness perception
000172707 6531_ $$aneural computation
000172707 6531_ $$aMonkey Visual-Cortex
000172707 6531_ $$aIllusory Contours
000172707 6531_ $$aSubjective Contours
000172707 6531_ $$aGround Separation
000172707 6531_ $$aNeural Dynamics
000172707 6531_ $$aFilling-In
000172707 6531_ $$aArea V2
000172707 6531_ $$aPsychophysical Evidence
000172707 6531_ $$aPerceptual Completion
000172707 6531_ $$aSpatial Arrangement
000172707 700__ $$aKogo, Naoki
000172707 700__ $$0244088$$g182325$$aStrecha, Christoph
000172707 700__ $$aVan Gool, Luc
000172707 700__ $$aWagemans, Johan
000172707 773__ $$j117$$tPsychological Review$$q406-439
000172707 909C0 $$xU10659$$0252087$$pCVLAB
000172707 909CO $$pIC$$particle$$ooai:infoscience.tind.io:172707
000172707 917Z8 $$x182325
000172707 937__ $$aEPFL-ARTICLE-172707
000172707 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000172707 980__ $$aARTICLE