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Liang Fang, Stephen Grossberg; How Are the Surface Lightnesses of Complex Stereograms Assigned to the Correct Depths?. Journal of Vision 2004;4(8):582. doi: 10.1167/4.8.582.
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When we view a 3D scene, object features are seen as part of 3D surfaces infused with lightness and color at the correct depths. Most models of stereopsis and 3D vision do not include plausible mechanisms for how this happens. A 3D LAMINART model has recently proposed how laminar cortical mechanisms interact to create such 3D surface percepts using interactions between boundary and surface representations (Grossberg and Howe, 2003; Grossberg and Swaminathan, 2003). In particular, the model clarifies how interactions between layers 4, 3B, and 2/3A in V1 and V2 contribute to stereopsis, and proposes how binocular and monocular information combine in V2 and V4 to form 3D boundary and surface representations. This work proposed a solution to the Correspondence Problem, but restricted itself to relatively simple displays, ranging from Panum's limiting case and contrast variations of dichoptic masking to the Venetian blind illusion, da Vinci stereopsis, and the Necker cube. The present work consistently extends the model to explain how images with multiple potential false binocular matches, such as dense stereograms, generate the correct 3D surfaces representations of figures and their backgrounds. The model clarifies and important role for surface-to-boundary feedback to eliminate many spurious boundaries that could otherwise interfere with correct object recognition at inferotemporal cortical processing stages. In particular, a disparity filter and 3D cooperative-competitive grouping laws are predicted to interact with 3D surface filling-in operations via feedback between V2 pale and thin stripes, respectively, to overcome this problem. This interaction also triggers figure-ground separation of occluded and occluding objects, and helps to convert the complementary rules for boundary and surface formation (Grossberg, 1994) into a consistent visual percept.
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