Hypothesis Verification Using Parametric Models and Active Vision Strategies

This paper proposes a methodology for determining the shape and ultimately the functionality of objects from intensity images; 2D analytic functions are used to track 3D features during known camera motions. Three analytic functions are proposed that describe the relationship between pairs of points that are either stationary or moving depending on whether the points are on occluding boundaries or otherwise. Many of the problems of correspondence are reduced by using foveation, known camera motion, and active vision methods. The three analytic functions are shown to enable hypothesis refinement of the functionality of a number of 3D objects without full 3D information about the shape.

[1]  John K. Tsotsos,et al.  Active object recognition , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Svetha Venkatesh,et al.  Function directed geometric model-based active visual strategies , 1993 .

[3]  Fausto Giunchiglia,et al.  FUR: Understanding functional reasoning , 1989, Int. J. Intell. Syst..

[4]  J. Gibson The Ecological Approach to Visual Perception , 1979 .

[5]  Alistair J. Bray Tracking Curved Objects by Perspective Inversion , 1991 .

[6]  Yiannis Aloimonos,et al.  Obstacle Avoidance Using Flow Field Divergence , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Alex Pentland,et al.  Shape Information From Shading: A Theory About Human Perception , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[8]  Takeo Kanade,et al.  Shape and motion without depth , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[9]  Tieniu Tan,et al.  3D structure and motion estimation from 2D image sequences , 1993, Image Vis. Comput..

[10]  Y. Sato,et al.  Active vision with two differentiated visual fields , 1992, [1992] Proceedings. 11th IAPR International Conference on Pattern Recognition.

[11]  Azriel Rosenfeld,et al.  Purposive recognition: an active and qualitative approach , 1992, Other Conferences.

[12]  Chris Harris,et al.  Tracking with rigid models , 1993 .

[13]  Dana H. Ballard,et al.  Eye Fixation And Early Vision: Kinetic Depth , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[14]  Michael J. Swain,et al.  Promising directions in active vision , 1993, International Journal of Computer Vision.

[15]  David W. Murray,et al.  From an Image Sequence to a Recognized Polyhedral Object , 1987, Alvey Vision Conference.

[16]  Giulio Sandini,et al.  Active Tracking Strategy for Monocular Depth Inference over Multiple Frames , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Michel Dhome,et al.  Spatial Localization Of Modelled Objects Of Revolution In Monocular Perspective Vision , 1990, ECCV.

[18]  Steven D. Blostein,et al.  Detecting small, moving objects in image sequences using sequential hypothesis testing , 1991, IEEE Trans. Signal Process..

[19]  Wayne D. Gray,et al.  Basic objects in natural categories , 1976, Cognitive Psychology.

[20]  Thomas S. Huang,et al.  Motion and Structure from Image Sequences , 1992 .

[21]  Kevin W. Bowyer,et al.  Generic recognition through qualitative reasoning about 3-D shape and object function , 1991, CVPR.

[22]  Dana H. Ballard,et al.  Principles of animate vision , 1992, CVGIP Image Underst..

[23]  Geoffrey D. Sullivan,et al.  Model-Based Tracking , 2011, BMVC.

[24]  Christopher G. Harris,et al.  3D positional integration from image sequences , 1988, Image Vis. Comput..

[25]  Azriel Rosenfeld,et al.  Recognition by Functional Parts , 1995, Comput. Vis. Image Underst..

[26]  Marie-Christine Jaulent,et al.  Object structure and action requirements: A compatibility model for functional recognition , 1991, Int. J. Intell. Syst..

[27]  David G. Lowe,et al.  Perceptual Organization and Visual Recognition , 2012 .

[28]  Rachid Deriche,et al.  Tracking line segments , 1990, Image Vis. Comput..

[29]  Geoff A. W. West,et al.  Nonparametric Segmentation of Curves into Various Representations , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Randall Davis,et al.  Diagnostic Reasoning Based on Structure and Behavior , 1984, Artif. Intell..

[31]  Michael Brady,et al.  Generating and Generalizing Models of Visual Objects , 1987, Artif. Intell..

[32]  M. Ali Taalebinezhaad Direct robot motion vision by fixation , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[33]  Michael Brady,et al.  The Mechanic's Mate , 1984, ECAI.

[34]  John K. Tsotsos,et al.  An Attentional Prototype for Early Vision , 1992, ECCV.

[35]  Sharon A. Stansfield,et al.  Representing generic objects for exploration and recognition , 1988, Proceedings. 1988 IEEE International Conference on Robotics and Automation.

[36]  David G. Lowe,et al.  Robust model-based motion tracking through the integration of search and estimation , 1992, International Journal of Computer Vision.

[37]  Yiannis Aloimonos,et al.  Purposive and qualitative active vision , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

[38]  Michael R. Lowry,et al.  Learning Physical Descriptions From Functional Definitions, Examples, and Precedents , 1983, AAAI.

[39]  M. Minsky The Society of Mind , 1986 .

[40]  Kjell Brunnström,et al.  Active fixation for scene exploration , 1996, International Journal of Computer Vision.

[41]  Ruzena Bajcsy,et al.  Active and exploratory perception , 1992, CVGIP Image Underst..

[42]  Allen Newell,et al.  A Model for Functional Reasoning in Design , 1971, IJCAI.

[43]  Svetha Venkatesh,et al.  Emerging hypothesis verification using function-based geometric models and active vision strategies , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[44]  Christopher M. Brown,et al.  Where to Look Next Using a Bayes Net: Incorporating Geometric Relations , 1992, ECCV.

[45]  Virginio Cantoni,et al.  Human and Machine Perception 2 , 2012, Springer US.

[46]  Dana H. Ballard,et al.  Computer Vision , 1982 .