Multi-scale lines and edges in V1 and beyond: Brightness, object categorization and recognition, and consciousness

In this paper we present an improved model for line and edge detection in cortical area V1. This model is based on responses of simple and complex cells, and it is multi-scale with no free parameters. We illustrate the use of the multi-scale line/edge representation in different processes: visual reconstruction or brightness perception, automatic scale selection and object segregation. A two-level object categorization scenario is tested in which pre-categorization is based on coarse scales only and final categorization on coarse plus fine scales. We also present a multi-scale object and face recognition model. Processing schemes are discussed in the framework of a complete cortical architecture. The fact that brightness perception and object recognition may be based on the same symbolic image representation is an indication that the entire (visual) cortex is involved in consciousness.

[1]  T Lourens,et al.  Biologically Motivated Approach to Face Recognition , 1993, IWANN.

[2]  J. M. Hans du Buf,et al.  Responses of simple cells: events, interferences, and ambiguities , 1993, Biological Cybernetics.

[3]  Antonio Torralba,et al.  Building the gist of a scene: the role of global image features in recognition. , 2006, Progress in brain research.

[4]  Narendra Ahuja,et al.  Detecting Faces in Images: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Bülent Sankur,et al.  ARTICLE IN PRESS Image and Vision Computing xx (2005) 1–9 www.elsevier.com/locate/imavis , 2004 .

[6]  Tai Sing Lee,et al.  Image Representation Using 2D Gabor Wavelets , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Mark A. Georgeson,et al.  Visual perception : physiology, psychology, & ecology , 2003 .

[8]  F. Hamker The reentry hypothesis: the putative interaction of the frontal eye field, ventrolateral prefrontal cortex, and areas V4, IT for attention and eye movement. , 2005, Cerebral cortex.

[9]  J. M. Hans du Buf,et al.  Simultaneous Detection of Lines and Edges Using Compound Gabor Filters , 2000, Int. J. Pattern Recognit. Artif. Intell..

[10]  M. Bar A Cortical Mechanism for Triggering Top-Down Facilitation in Visual Object Recognition , 2003, Journal of Cognitive Neuroscience.

[11]  Tony Lindeberg,et al.  Scale-Space Theory in Computer Vision , 1993, Lecture Notes in Computer Science.

[12]  D. Hubel Eye, brain, and vision , 1988 .

[13]  David J. Fleet,et al.  Phase-based disparity measurement , 1991, CVGIP Image Underst..

[14]  Christoph Rasche The Making of a Neuromorphic Visual System , 2004 .

[15]  Takio Kurita,et al.  Face matching through information theoretical attention points and its applications to face detection and classification , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[16]  David J. Field,et al.  Contour integration by the human visual system: Evidence for a local “association field” , 1993, Vision Research.

[17]  Lucas J. van Vliet,et al.  Line and edge detection by symmetry filters , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,.

[18]  S. Greenfield,et al.  The neuroscience of consciousness , 2006, Acta Neuropsychiatrica.

[19]  A. Noë,et al.  A sensorimotor account of vision and visual consciousness. , 2001, The Behavioral and brain sciences.

[20]  Erhardt Barth,et al.  Endstopped operators based on iterated nonlinear center-surround inhibition , 1998, Electronic Imaging.

[21]  João M. F. Rodrigues,et al.  Multi-scale Keypoints in V1 and Face Detection , 2005, BVAI.

[22]  Dale Purves,et al.  The statistical structure of natural light patterns determines perceived light intensity. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Bernt Schiele,et al.  Analyzing appearance and contour based methods for object categorization , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[24]  Norbert Krüger,et al.  ORASSYLL: Object Recognition with Autonomously Learned and Sparse Symbolic Representations Based on Metrically Organized Local Line Detectors , 2000, Comput. Vis. Image Underst..

[25]  Stefan Fischer,et al.  Modeling brightness perception and syntactical image coding , 1995 .

[26]  Tomaso A. Poggio,et al.  CBF: A New Framework for Object Categorization in Cortex , 2000, Biologically Motivated Computer Vision.

[27]  Bruce G Cumming,et al.  Does depth perception require vertical-disparity detectors? , 2006, Journal of vision.

[28]  J. O'Regan,et al.  Solving the "real" mysteries of visual perception: the world as an outside memory. , 1992, Canadian journal of psychology.

[29]  Nicolai Petkov,et al.  Contour detection based on nonclassical receptive field inhibition , 2003, IEEE Trans. Image Process..

[30]  H. Neumann,et al.  A recurrent model of contour integration in primary visual cortex. , 2008, Journal of vision.

[31]  A. J. Mistlin,et al.  Visual cells in the temporal cortex sensitive to face view and gaze direction , 1985, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[32]  Nicolai Petkov,et al.  Person identification based on multiscale matching of cortical images , 1995, HPCN Europe.

[33]  S. M. Steve SUSAN - a new approach to low level image processing , 1997 .

[34]  João M. F. Rodrigues,et al.  Face Recognition by Cortical Multi-scale Line and Edge Representations , 2006, ICIAR.

[35]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[36]  F. Kingdom,et al.  White's effect: A dual mechanism , 1989, Vision Research.

[37]  Antonio Torralba,et al.  Top-down control of visual attention in object detection , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[38]  Nicolai Petkov,et al.  Lateral inhibition in cortical filters , 1993 .

[39]  N. Drasdo Eye, brain, and vision David H. Hubel Scientific American Library Book — distributed by W. H. Freeman, New York, £15.95 , 1990 .

[40]  J. M. Hans du Buf,et al.  Improved grating and bar cell models in cortical area V1 and texture coding , 2007, Image Vis. Comput..

[41]  Peter Kovesi,et al.  Image Features from Phase Congruency , 1995 .

[42]  João M. F. Rodrigues,et al.  Face Segregation and Recognition by Cortical Multi-scale Line and Edge Coding , 2006, PRIS.

[43]  Iain D Gilchrist,et al.  Oculomotor capture by transient events: a comparison of abrupt onsets, offsets, motion, and flicker. , 2008, Journal of vision.

[44]  V. Bruce,et al.  Visual perception: physiology, psychology and ecology. Fourth edition , 2003 .

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

[46]  Olaf Kübler,et al.  Simulation of neural contour mechanisms: from simple to end-stopped cells , 1992, Vision Research.

[47]  Steven W. Zucker,et al.  Local Scale Control for Edge Detection and Blur Estimation , 1996, ECCV.

[48]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[49]  Jang-Kyoo Shin,et al.  Face detection using biologically motivated saliency map model , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[50]  Dennis M. Levi,et al.  Global contour processing in amblyopia , 2007, Vision Research.

[51]  Hans du Buf,et al.  Modeling Brightness Perception , 2001 .

[52]  Rüdiger von der Heydt,et al.  A computational model of neural contour processing: Figure-ground segregation and illusory contours , 1993, 1993 (4th) International Conference on Computer Vision.

[53]  D. Berson,et al.  Strange vision: ganglion cells as circadian photoreceptors , 2003 .

[54]  C. Koch,et al.  A framework for consciousness , 2003, Nature Neuroscience.

[55]  I Biederman,et al.  Neurocomputational bases of object and face recognition. , 1997, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[56]  Zhaoping Li V1 mechanisms and some figure-ground and border effects. , 2003, Journal of physiology, Paris.

[57]  C. Koch,et al.  Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.

[58]  Laurent Itti,et al.  Gist: A Mobile Robotics Application of Context-Based Vision in Outdoor Environment , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[59]  Ee-Chien Chang,et al.  Edge directed filter based error concealment for wavelet-based images , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[60]  Nicolai Petkov,et al.  Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli: bar and grating cells , 1997, Biological Cybernetics.

[61]  Hugh R. Wilson,et al.  A deficit in strabismic amblyopia for global shape detection , 1999, Vision Research.

[62]  E. Rolls,et al.  A Neurodynamical cortical model of visual attention and invariant object recognition , 2004, Vision Research.

[63]  H. Abdi,et al.  What Represents a Face? A Computational Approach for the Integration of Physiological and Psychological Data , 1997, Perception.

[64]  R. Haber,et al.  Visual Perception , 2018, Encyclopedia of Database Systems.

[65]  Derrick J. Parkhurst,et al.  Modeling the role of salience in the allocation of overt visual attention , 2002, Vision Research.

[66]  João M. F. Rodrigues,et al.  Visual Cortex Frontend: Integrating Lines, Edges, Keypoints, and Disparity , 2004, ICIAR.

[67]  L. Pessoa,et al.  Mach Bands: How Many Models are Possible? Recent Experimental Findings and Modeling Attempts , 1996, Vision Research.

[68]  João Rodrigues,et al.  Multi-scale keypoints in V1 and beyond: object segregation, scale selection, saliency maps and face detection. , 2006, Bio Systems.

[69]  Roelfsema Pieter Cortical algorithms for perceptual grouping , 2008 .

[70]  Arnaud Delorme,et al.  Face identification using one spike per neuron: resistance to image degradations , 2001, Neural Networks.

[71]  M. Bar Visual objects in context , 2004, Nature Reviews Neuroscience.

[72]  H. Offret Computational Maps in the Visual Cortex , 2006 .

[73]  Sudeep Sarkar,et al.  Robust Visual Method for Assessing the Relative Performance of Edge-Detection Algorithms , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[74]  A. A. El-Harby,et al.  Face Recognition: A Literature Review , 2008 .

[75]  N. Qian Binocular Disparity and the Perception of Depth , 1997, Neuron.

[76]  João M. F. Rodrigues,et al.  Improved line/edge detection and visual reconstruction , 2005 .

[77]  I. Ohzawa,et al.  Encoding of binocular disparity by complex cells in the cat's visual cortex. , 1996, Journal of neurophysiology.

[78]  Christian J. Van den Branden Lambrecht,et al.  Vision Models and Applications to Image and Video Processing , 2001 .

[79]  Ronald A. Rensink The Dynamic Representation of Scenes , 2000 .

[80]  N. Krüger Multi–modal Primitives: Local, Condensed, and Semantically Rich Visual Descriptors and the Formalisation of Contextual Information , 2007 .

[81]  J. M. Hans du Buf,et al.  Ramp edges, Mach bands, and the functional significance of the simple cell assembly , 1994, Biological Cybernetics.