What We Can Learn From the Primate’s Visual System

In this review, we discuss the impact (or lack thereof) biologically motivated vision has had on computer vision in the last decades. We then summarize a number of computer vision and robotic problems for which biological models can give indications for how these can be addressed. Then we summarize important findings about the primate’s visual system and draw a number of conclusions for the development of algorithms from these findings.

[1]  Vincent Lepetit,et al.  Gradient Response Maps for Real-Time Detection of Textureless Objects , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Jitendra Malik,et al.  Contour Continuity in Region Based Image Segmentation , 1998, ECCV.

[3]  Mohammed Bennamoun,et al.  Three-Dimensional Model-Based Object Recognition and Segmentation in Cluttered Scenes , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Sarah J Parsons,et al.  Guest Editors , 2012, Oncogene.

[5]  Andrew Zisserman,et al.  Return of the Devil in the Details: Delving Deep into Convolutional Nets , 2014, BMVC.

[6]  Helge J. Ritter,et al.  Real-time hierarchical scene segmentation and classification , 2014, 2014 IEEE-RAS International Conference on Humanoid Robots.

[7]  Gregory D. Hager,et al.  Scene parsing using a prior world model , 2011, Int. J. Robotics Res..

[8]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[10]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[11]  Ian D. Reid,et al.  Dense Reconstruction Using 3D Object Shape Priors , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  I. Biederman,et al.  Dynamic binding in a neural network for shape recognition. , 1992, Psychological review.

[13]  Florentin Wörgötter,et al.  International Journal of Humanoid Robotics c ○ World Scientific Publishing Company Visual Primitives: Local, Condensed, Semantically Rich Visual Descriptors and their Applications in Robotics , 2022 .

[14]  Tom Drummond,et al.  Faster and Better: A Machine Learning Approach to Corner Detection , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  N. Krüger,et al.  Statistical and Deterministic Regularities: Utilization of Motion and Grouping in Biological and Artificial Visual Systems , 2004 .

[16]  Horst Bischof,et al.  Dense reconstruction on-the-fly , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Derek Hoiem,et al.  Indoor Segmentation and Support Inference from RGBD Images , 2012, ECCV.

[18]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[19]  Sinan Kalkan,et al.  Deep Hierarchies in the Primate Visual Cortex: What Can We Learn for Computer Vision? , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  P. Greenberg,et al.  Retinal implants: a systematic review , 2014, British Journal of Ophthalmology.

[21]  Sven J. Dickinson,et al.  Contour Grouping and Abstraction Using Simple Part Models , 2010, ECCV.

[22]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[23]  Sven J. Dickinson,et al.  The Role of Mid-Level Shape Priors in Perceptual Grouping and Image Abstraction , 2013, Shape Perception in Human and Computer Vision.

[24]  K Tanaka,et al.  Neuronal mechanisms of object recognition. , 1993, Science.

[25]  Henrik I. Christensen,et al.  Computational visual attention systems and their cognitive foundations: A survey , 2010, TAP.

[26]  Geoffrey E. Hinton,et al.  Learning representations by back-propagation errors, nature , 1986 .

[27]  Gary R. Bradski,et al.  Fast 3D recognition and pose using the Viewpoint Feature Histogram , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[28]  Laurenz Wiskott,et al.  Slow feature analysis yields a rich repertoire of complex cell properties. , 2005, Journal of vision.

[29]  Dhiraj Joshi,et al.  Object Categorization: Computer and Human Vision Perspectives , 2008 .

[30]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[31]  Dieter Fox,et al.  RGB-D object discovery via multi-scene analysis , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[32]  Norbert Krüger A Required Paradigm Shift in Today’s Vision Research , 2014, KI - Künstliche Intelligenz.

[33]  G. Klein,et al.  Parallel Tracking and Mapping for Small AR Workspaces , 2007, 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality.

[34]  Ali Borji,et al.  Probabilistic learning of task-specific visual attention , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[35]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[36]  R. von der Heydt,et al.  Illusory contours and cortical neuron responses. , 1984, Science.

[37]  Vincent Lepetit,et al.  DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Daniel Cremers,et al.  Real-Time Dense Geometry from a Handheld Camera , 2010, DAGM-Symposium.

[39]  Bernard Chazelle,et al.  Matching 3D models with shape distributions , 2001, Proceedings International Conference on Shape Modeling and Applications.

[40]  Larry S. Davis,et al.  Model-based object pose in 25 lines of code , 1992, International Journal of Computer Vision.

[41]  David G. Lowe,et al.  What and Where: 3D Object Recognition with Accurate Pose , 2006, Toward Category-Level Object Recognition.

[42]  Dima Damen,et al.  Detecting Carried Objects in Short Video Sequences , 2008, ECCV.

[43]  Samy Bengio,et al.  Guest Editors' Introduction: Special Section on Learning Deep Architectures , 2013, IEEE Trans. Pattern Anal. Mach. Intell..

[44]  John K. Tsotsos,et al.  50 Years of object recognition: Directions forward , 2013, Comput. Vis. Image Underst..

[45]  Federico Tombari,et al.  Unique Signatures of Histograms for Local Surface Description , 2010, ECCV.

[46]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[47]  BorjiAli,et al.  State-of-the-Art in Visual Attention Modeling , 2013 .

[48]  Sanja Fidler,et al.  A Coarse-to-Fine Taxonomy of Constellations for Fast Multi-class Object Detection , 2010, ECCV.

[49]  Fei-Fei Li,et al.  Detecting Avocados to Zucchinis: What Have We Done, and Where Are We Going? , 2013, 2013 IEEE International Conference on Computer Vision.

[50]  Danica Kragic,et al.  Birth of the Object: Detection of Objectness and Extraction of Object Shape through Object-Action complexes , 2008, Int. J. Humanoid Robotics.

[51]  Laurent Itti,et al.  An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[52]  D. Fox,et al.  Manipulator and Object Tracking for In Hand Model Acquisition , 2010 .

[53]  Albert Yonas,et al.  Self-Produced Locomotion and the Development of Responsiveness to Linear Perspective and Texture Gradients. , 1989 .

[54]  R. Weale Vision. A Computational Investigation Into the Human Representation and Processing of Visual Information. David Marr , 1983 .

[55]  D. Kersten,et al.  Border Ownership Selectivity in Human Early Visual Cortex and its Modulation by Attention , 2009, The Journal of Neuroscience.

[56]  Konrad Paul Kording,et al.  Processing of complex stimuli and natural scenes in the visual cortex , 2004, Current Opinion in Neurobiology.

[57]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[58]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[59]  David G. Lowe,et al.  Three-Dimensional Object Recognition from Single Two-Dimensional Images , 1987, Artif. Intell..

[60]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[61]  Kim L. Boyer,et al.  Perceptual organization in computer vision: a review and a proposal for a classificatory structure , 1993, IEEE Trans. Syst. Man Cybern..

[62]  Joan Bruna,et al.  Intriguing properties of neural networks , 2013, ICLR.

[63]  Nicolas Y. Masse,et al.  Reach and grasp by people with tetraplegia using a neurally controlled robotic arm , 2012, Nature.

[64]  Kim L. Boyer,et al.  Guest Editors' Introduction: Perceptual Organization in Computer Vision: Status, Challenges, and Potential , 1999, Comput. Vis. Image Underst..

[65]  D. Hubel,et al.  Anatomical Demonstration of Columns in the Monkey Striate Cortex , 1969, Nature.

[66]  Norbert Krüger,et al.  Symbols as Self-emergent Entities in an Optimization Process of Feature Extraction and Predictions , 2006, Biological Cybernetics.

[67]  Sven J. Dickinson,et al.  Object Categorization: The Evolution of Object Categorization and the Challenge of Image Abstraction , 2009 .

[68]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[69]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[70]  Richard Szeliski,et al.  Efficient High-Resolution Stereo Matching Using Local Plane Sweeps , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[71]  Richard Szeliski,et al.  Building Rome in a day , 2009, ICCV.

[72]  Rares Ambrus,et al.  Meta-rooms: Building and maintaining long term spatial models in a dynamic world , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[73]  S. Palmer,et al.  A century of Gestalt psychology in visual perception: I. Perceptual grouping and figure-ground organization. , 2012, Psychological bulletin.

[74]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[75]  Paul Newman,et al.  Appearance-only SLAM at large scale with FAB-MAP 2.0 , 2011, Int. J. Robotics Res..

[76]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[77]  Federico Tombari,et al.  SHOT: Unique signatures of histograms for surface and texture description , 2014, Comput. Vis. Image Underst..

[78]  Norbert Krüger,et al.  Special Issue on Bio-inspired Vision Systems , 2014, KI - Künstliche Intelligenz.

[79]  Andrew J. Davison,et al.  DTAM: Dense tracking and mapping in real-time , 2011, 2011 International Conference on Computer Vision.

[80]  M. Arterberry,et al.  The Cradle of Knowledge: Development of Perception in Infancy , 1998 .

[81]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[82]  Andrew Blake,et al.  Efficient Dense Stereo with Occlusions for New View-Synthesis by Four-State Dynamic Programming , 2006, International Journal of Computer Vision.

[83]  Andrew E. Johnson,et al.  Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[84]  Markus Vincze,et al.  Automation of “ground truth” annotation for multi-view RGB-D object instance recognition datasets , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[85]  Bernard Chazelle,et al.  Shape distributions , 2002, TOGS.

[86]  Markus Vincze,et al.  Learning of perceptual grouping for object segmentation on RGB-D data , 2014, J. Vis. Commun. Image Represent..

[87]  Davide Scaramuzza,et al.  REMODE: Probabilistic, monocular dense reconstruction in real time , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[88]  David J. Freedman,et al.  Experience-dependent representation of visual categories in parietal cortex , 2006, Nature.

[89]  Andrew J. Davison,et al.  Live dense reconstruction with a single moving camera , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[90]  Olivier Faugeras,et al.  Three-Dimensional Computer Vision , 1993 .