Texture segregation by visual cortex: Perceptual grouping, attention, and learning Export

@article{Bhatt2007Texture, abstract = {A neural model called dARTEX is proposed of how laminar interactions in the visual cortex may learn and recognize object texture and form boundaries. The model unifies five interacting processes: region-based texture classification, contour-based boundary grouping, surface filling-in, spatial attention, and object attention. The model shows how form boundaries can determine regions in which surface filling-in occurs; how surface filling-in interacts with spatial attention to generate a form-fitting distribution of spatial attention, or attentional shroud; how the strongest shroud can inhibit weaker shrouds; and how the winning shroud regulates learning of texture categories, and thus the allocation of object attention. The model can discriminate abutted textures with blurred boundaries and is sensitive to texture boundary attributes like discontinuities in orientation and texture flow curvature as well as to relative orientations of texture elements. The model quantitatively fits the Ben-Shahar and Zucker [Ben-Shahar, O. \& Zucker, S. (2004). Sensitivity to curvatures in orientation-based texture segmentation. Vision Research, 44, 257-277] human psychophysical data on orientation-based textures. Surface-based attentional shrouds improve texture learning and classification: Brodatz texture classification rate varies from 95.1\% to 98.6\% with correct attention, and from 74.1\% to 75.5\% without attention. Object boundary output of the model in response to photographic images is compared to computer vision algorithms and human segmentations.}, author = {Bhatt, Rushi and Carpenter, Gail A. and Grossberg, Stephen}, citeulike-article-id = {2817520}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.visres.2007.07.013}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/17904187}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=17904187}, citeulike-linkout-3 = {http://www.sciencedirect.com/science/article/B6T0W-4PS63H0-1/1/62621c70a67077eb4409a87b092af486}, comment = {Already read intro to thesis}, doi = {10.1016/j.visres.2007.07.013}, journal = {Vision Research}, keywords = {art, artmap, attention, facade, figureground, model, texture}, month = {November}, number = {25}, pages = {3173--3211}, posted-at = {2008-05-20 20:44:55}, priority = {2}, title = {Texture segregation by visual cortex: Perceptual grouping, attention, and learning}, url = {http://dx.doi.org/10.1016/j.visres.2007.07.013}, volume = {47}, year = {2007} }

TY - JOUR ID - Bhatt2007Texture L3 - citeulike-article-id:2817520 N2 - A neural model called dARTEX is proposed of how laminar interactions in the visual cortex may learn and recognize object texture and form boundaries. The model unifies five interacting processes: region-based texture classification, contour-based boundary grouping, surface filling-in, spatial attention, and object attention. The model shows how form boundaries can determine regions in which surface filling-in occurs; how surface filling-in interacts with spatial attention to generate a form-fitting distribution of spatial attention, or attentional shroud; how the strongest shroud can inhibit weaker shrouds; and how the winning shroud regulates learning of texture categories, and thus the allocation of object attention. The model can discriminate abutted textures with blurred boundaries and is sensitive to texture boundary attributes like discontinuities in orientation and texture flow curvature as well as to relative orientations of texture elements. The model quantitatively fits the Ben-Shahar and Zucker [Ben-Shahar, O. & Zucker, S. (2004). Sensitivity to curvatures in orientation-based texture segmentation. Vision Research, 44, 257-277] human psychophysical data on orientation-based textures. Surface-based attentional shrouds improve texture learning and classification: Brodatz texture classification rate varies from 95.1% to 98.6% with correct attention, and from 74.1% to 75.5% without attention. Object boundary output of the model in response to photographic images is compared to computer vision algorithms and human segmentations. IS - 25 JF - Vision Research EP - 3211 TI - Texture segregation by visual cortex: Perceptual grouping, attention, and learning VL - 47 SP - 3173 KW - art KW - artmap KW - attention KW - facade KW - figureground KW - model KW - texture N1 - Already read intro to thesis AU - Bhatt, Rushi AU - Carpenter, Gail AU - Grossberg, Stephen PY - 2007/11// UR - http://dx.doi.org/10.1016/j.visres.2007.07.013 ER -