Early Cognitive Vision as a Frontend for Cognitive Systems

We discuss the need of an elaborated in-between stage bridging early vision and cognitive vision which we call ‘Early Cognitive Vision’ (ECV). This stage provides semantically rich, disambiguated and largely task independent scene representations which can be used in many contexts. In addition, the ECV stage is important for generalization processes across objects and actions. We exemplify this at a concrete realisation of an ECV system that has already been used in variety of application domains.

[1]  O. Reiser,et al.  Principles Of Gestalt Psychology , 1936 .

[2]  W. Pitts,et al.  What the Frog's Eye Tells the Frog's Brain , 1959, Proceedings of the IRE.

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

[4]  W. Köhler.  Gestalt Psychology: An Introduction to New Concepts in Modern Psychology , 1970 .

[5]  D. Hubel,et al.  Ordered arrangement of orientation columns in monkeys lacking visual experience , 1974, The Journal of comparative neurology.

[6]  G. Granlund.  In search of a general picture processing operator , 1978 .

[7]  W. Eric L. Grimson,et al.  Surface consistency constraints in vision , 1982, Comput. Graph. Image Process..

[8]  Rodney A. Brooks,et al.  Model-Based Three-Dimensional Interpretations of Two-Dimensional Images , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  J. P. Jones,et al.  An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. , 1987, Journal of neurophysiology.

[10]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[11]  Yehezkel Lamdan,et al.  Geometric Hashing: A General And Efficient Model-based Recognition Scheme , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[12]  John Y. Aloimonos,et al.  Unification and integration of visual modules: an extension of the Marr Paradigm , 1989 .

[13]  H. Tokuno,et al.  Central visual pathways in the mole () , 1989 .

[14]  C. Zetzsche,et al.  Fundamental limits of linear filters in the visual processing of two-dimensional signals , 1990, Vision Research.

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

[16]  Hans Knutsson,et al.  Signal processing for computer vision , 1994 .

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

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

[19]  David Mumford,et al.  Statistics of range images , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[20]  Refractor.  Vision , 2000, The Lancet.

[21]  Bodo Rosenhahn,et al.  Adaptive Pose Estimation for Different Corresponding Entities , 2002, DAGM-Symposium.

[22]  Pietro Perona,et al.  Object class recognition by unsupervised scale-invariant learning , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[23]  Dale Purves,et al.  Image/source statistics of surfaces in natural scenes , 2003, Network.

[24]  Tai Sing Lee,et al.  Statistical correlations between two-dimensional images and three-dimensional structures in natural scenes. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[25]  Kostas Daniilidis,et al.  Linear Pose Estimation from Points or Lines , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Markus Lappe,et al.  Biologically Motivated Multi-modal Processing of Visual Primitives , 2003 .

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

[28]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[29]  Alexei A. Efros,et al.  Discovering objects and their location in images , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[30]  Cordelia Schmid,et al.  The 2005 PASCAL Visual Object Classes Challenge , 2005, MLCW.

[31]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  Jianguo Zhang,et al.  The PASCAL Visual Object Classes Challenge , 2006 .

[33]  Florentin Wörgötter,et al.  Statistical Analysis of Local 3D Structure in 2D Images , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

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

[35]  Cordelia Schmid,et al.  Spatial Weighting for Bag-of-Features , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[36]  Sanja Fidler,et al.  Towards Scalable Representations of Object Categories: Learning a Hierarchy of Parts , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[37]  Bernt Schiele,et al.  Robust Object Detection with Interleaved Categorization and Segmentation , 2008, International Journal of Computer Vision.

[38]  Antonio Torralba,et al.  Sharing Visual Features for Multiclass and Multiview Object Detection , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  Kevin Skadron,et al.  A performance study of general-purpose applications on graphics processors using CUDA , 2008, J. Parallel Distributed Comput..

[40]  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.

[41]  David A. McAllester,et al.  A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[42]  Yan Shi,et al.  A Signal-Symbol Loop Mechanism for Enhanced Edge Extraction , 2008, VISAPP.

[43]  Florentin Wörgötter,et al.  Accumulated Visual Representation for Cognitive Vision , 2008, BMVC.

[44]  Norbert Krüger,et al.  Comparison of Point and Line Features and Their Combination for Rigid Body Motion Estimation , 2009, Statistical and Geometrical Approaches to Visual Motion Analysis.

[45]  Michael Felsberg,et al.  Continuous dimensionality characterization of image structures , 2009, Image Vis. Comput..

[46]  Justus H. Piater,et al.  Learning Objects and Grasp Affordances through Autonomous Exploration , 2009, ICVS.

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

[48]  Justus H. Piater,et al.  A Probabilistic Framework for 3D Visual Object Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[49]  Sinan Kalkan,et al.  Using multi-modal 3D contours and their relations for vision and robotics , 2010, J. Vis. Commun. Image Represent..

[50]  Fabio Solari,et al.  A compact harmonic code for early vision based on anisotropic frequency channels , 2010, Comput. Vis. Image Underst..

[51]  Marc M. Van Hulle,et al.  A two-level real-time vision machine combining coarse- and fine-grained parallelism , 2010, Journal of Real-Time Image Processing.

[52]  Sinan Kalkan,et al.  Disparity disambiguation by fusion of signal- and symbolic-level information , 2010, Machine Vision and Applications.

[53]  Justus H. Piater,et al.  Refining grasp affordance models by experience , 2010, 2010 IEEE International Conference on Robotics and Automation.

[54]  Danica Kragic,et al.  A strategy for grasping unknown objects based on co-planarity and colour information , 2010, Robotics Auton. Syst..

[55]  Florentin Wörgötter,et al.  Visual Primitives: Local, Condensed, Semantically Rich Visual Descriptors and their Applications in Robotics , 2010, Int. J. Humanoid Robotics.

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

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