Early recurrence enables figure border ownership

The face-vase illusion introduced by Rubin demonstrates how one can switch back and forth between two different interpretations depending on how the figure outlines are assigned [1]. This border ownership assignment is an important step in the perception of forms. Zhou et al. [2] found neurons in the visual cortex whose responses not only depend on the local features present in their classical receptive fields, but also on their contextual information. Various models proposed that feedback from higher ventral areas or lateral connections could provide the required contextual information. However, some studies [3, 4, 5] ruled out the plausibility of models exclusively based on lateral connections. In addition, further evidence [6] suggests that ventral feedback even from V4 is not fast enough to provide context to border ownership neurons in either V1 or V2. As a result, the border ownership assignment mechanism in the brain is a mystery yet to be solved. Here, we test with computational simulations the hypothesis that the dorsal stream provides the global information to border ownership cells in the ventral stream. Our proposed model incorporates early recurrence from the dorsal pathway as well as lateral modulations within the ventral stream. Our simulation experiments show that our model border ownership neurons, similar to their biological counterparts, exhibit different responses to figures on either side of the border.

[1]  Stefan Mihalas,et al.  A model of proto-object based saliency , 2014, Vision Research.

[2]  Oliver W. Layton,et al.  Neural dynamics of feedforward and feedback processing in figure-ground segregation , 2014, Front. Psychol..

[3]  G. Orban,et al.  The Retinotopic Organization of the Human Middle Temporal Area MT/V5 and Its Cortical Neighbors , 2010, The Journal of Neuroscience.

[4]  Jeffrey M. Yau,et al.  Curvature processing dynamics in macaque area V4. , 2013, Cerebral cortex.

[5]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[6]  Ko Sakai,et al.  Consistent and robust determination of border ownership based on asymmetric surrounding contrast , 2012, Neural Networks.

[7]  John H. R. Maunsell,et al.  Coding of image contrast in central visual pathways of the macaque monkey , 1990, Vision Research.

[8]  C. Connor,et al.  Shape representation in area V4: position-specific tuning for boundary conformation. , 2001, Journal of neurophysiology.

[9]  Alexei A. Efros,et al.  Recovering Occlusion Boundaries from an Image , 2011, International Journal of Computer Vision.

[10]  Doris Y. Tsao,et al.  Consistency of Border-Ownership Cells across Artificial Stimuli, Natural Stimuli, and Stimuli with Ambiguous Contours , 2016, The Journal of Neuroscience.

[11]  Oliver W. Layton,et al.  Dynamic coding of border-ownership in visual cortex. , 2012, Journal of vision.

[12]  T. Albright Direction and orientation selectivity of neurons in visual area MT of the macaque. , 1984, Journal of neurophysiology.

[13]  John K. Tsotsos,et al.  Biologically Motivated Local Contextual Modulation Improves Low-Level Visual Feature Representations , 2012, ICIAR.

[14]  F. Qiu,et al.  Figure-ground mechanisms provide structure for selective attention , 2007, Nature Neuroscience.

[15]  Lawrence G. Roberts,et al.  Machine Perception of Three-Dimensional Solids , 1963, Outstanding Dissertations in the Computer Sciences.

[16]  Bo Wang,et al.  Early Recurrence Improves Edge Detection , 2013, BMVC.

[17]  R. Andersen,et al.  Functional analysis of human MT and related visual cortical areas using magnetic resonance imaging , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[18]  Li Zhaoping,et al.  Border Ownership from Intracortical Interactions in Visual Area V2 , 2005, Neuron.

[19]  Jonathan R. Williford,et al.  Figure-Ground Organization in Visual Cortex for Natural Scenes , 2016, eNeuro.

[20]  Ko Sakai,et al.  Surrounding Suppression and Facilitation in the Determination of Border Ownership , 2006, Journal of Cognitive Neuroscience.

[21]  R. Shapley,et al.  The primate retina contains two types of ganglion cells, with high and low contrast sensitivity. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[22]  R. von der Heydt,et al.  A neural model of figure-ground organization. , 2007, Journal of neurophysiology.

[23]  R Gattass,et al.  Visual area MT in the Cebus monkey: Location, visuotopic organization, and variability , 1989, The Journal of comparative neurology.

[24]  C. Connor,et al.  Responses to contour features in macaque area V4. , 1999, Journal of neurophysiology.

[25]  John K. Tsotsos,et al.  The Roles of Endstopped and Curvature Tuned Computations in a Hierarchical Representation of 2D Shape , 2012, PloS one.

[26]  D. J. Felleman,et al.  Receptive-field properties of neurons in middle temporal visual area (MT) of owl monkeys. , 1984, Journal of neurophysiology.

[27]  J. Bullier,et al.  Reaching beyond the classical receptive field of V1 neurons: horizontal or feedback axons? , 2003, Journal of Physiology-Paris.

[28]  R. von der Heydt,et al.  Analysis of the Context Integration Mechanisms Underlying Figure–Ground Organization in the Visual Cortex , 2010, The Journal of Neuroscience.

[29]  David Mumford,et al.  The 2.1-D sketch , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[30]  J. M. Hupé,et al.  Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons , 1998, Nature.

[31]  P Girard,et al.  Feedback connections act on the early part of the responses in monkey visual cortex. , 2001, Journal of neurophysiology.

[32]  Hans Supèr,et al.  Feed-Forward Segmentation of Figure-Ground and Assignment of Border-Ownership , 2010, PloS one.

[33]  J. Bullier Integrated model of visual processing , 2001, Brain Research Reviews.

[34]  Jitendra Malik,et al.  Figure/Ground Assignment in Natural Images , 2006, ECCV.

[35]  G. Orban,et al.  Response latency of macaque area MT/V5 neurons and its relationship to stimulus parameters. , 1999, Journal of neurophysiology.

[36]  R. Shapley,et al.  Spatial summation and contrast sensitivity of X and Y cells in the lateral geniculate nucleus of the macaque , 1981, Nature.

[37]  Rüdiger von der Heydt,et al.  The speed of context integration in the visual cortex. , 2011, Journal of neurophysiology.

[38]  Pieter R. Roelfsema,et al.  Boundary assignment in a recurrent network architecture , 2007, Vision Research.

[39]  Stephan Tschechne,et al.  Hierarchical representation of shapes in visual cortex—from localized features to figural shape segregation , 2014, Front. Comput. Neurosci..

[40]  R. von der Heydt,et al.  Coding of Border Ownership in Monkey Visual Cortex , 2000, The Journal of Neuroscience.

[41]  D H HUBEL,et al.  THE VISUAL CORTEX OF THE BRAIN. , 1963, Scientific American.

[42]  Synsoplevede Figurer. Studier i psykologisk Analyse , 1922 .

[43]  Jitendra Malik,et al.  Occlusion boundary detection and figure/ground assignment from optical flow , 2011, CVPR 2011.

[44]  Michael J. Black,et al.  Occlusion Boundary Detection via Deep Exploration of Context , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[45]  J. B. Levitt,et al.  Anatomical origins of the classical receptive field and modulatory surround field of single neurons in macaque visual cortical area V1. , 2002, Progress in brain research.

[46]  C. Connor,et al.  Population coding of shape in area V4 , 2002, Nature Neuroscience.

[47]  Scott L. Brincat,et al.  Dynamic Shape Synthesis in Posterior Inferotemporal Cortex , 2006, Neuron.

[48]  Steven W. Zucker,et al.  Relaxation Labeling: 25 Years and Still Iterating , 2001 .

[49]  John H. R. Maunsell,et al.  Physiological Evidence for Two Visual Subsystems , 1987 .

[50]  R Gattass,et al.  Visual topography of V1 in the Cebus monkey , 1987, The Journal of comparative neurology.

[51]  J. B. Levitt,et al.  Comparison of Spatial Summation Properties of Neurons in Macaque V1 and V2 , 2009, Journal of neurophysiology.

[52]  Alan L. Yuille,et al.  DOC: Deep OCclusion Estimation from a Single Image , 2015, ECCV.

[53]  Jitendra Malik,et al.  Local figure-ground cues are valid for natural images. , 2007, Journal of vision.