Recognizing object function through reasoning about partial shape descriptions and dynamic physical properties

Knowledge about required functionality of an object can be used as an effective representation for a generic object category (e.g. "chair", "cup", or "hammer"). This approach to object representation and recognition has recently become an active area of research. We explore a scenario in which a robot senses the environment to obtain an initial partial shape model of an object. If the information in this initial model is not sufficient to hypothesize a possible function for the object, then additional view(s) may be suggested. Once a possible function is hypothesized, a plan is formulated for interacting with the object to confirm that its material properties are compatible with the hypothesized function. The module for reasoning about partial shape models has been evaluated on over 200 shape models acquired from range images. The module for carrying out a function verification plan has been evaluated in a simulated environment using the ThingWorld (TW) system.

[1]  Dmitry B. Goldgof,et al.  Extracting a Valid Boundary Representation from a Segmented Range Image , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Thomas O. Binford,et al.  Survey of Model-Based Image Analysis Systems , 1982 .

[3]  Michael Brady,et al.  Artificial Intelligence and Robotics , 1985, Artif. Intell..

[4]  Ruzena Bajcsy,et al.  Interactive Recognition and Representation of Functionality , 1995, Comput. Vis. Image Underst..

[5]  Andrew W. Fitzgibbon,et al.  An Experimental Comparison of Range Image Segmentation Algorithms , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  François G. Pin,et al.  Autonomous mobile robot navigation and learning , 1989, Computer.

[7]  ARISTIDES A. G. REQUICHA,et al.  Representations for Rigid Solids: Theory, Methods, and Systems , 1980, CSUR.

[8]  Stephen M. Kosslyn,et al.  Naming pictures , 1990, J. Vis. Lang. Comput..

[9]  Kevin W. Bowyer,et al.  Three-dimensional shape representation , 1994 .

[10]  Diane J. Cook,et al.  Learning Fuzzy Membership Functions in a Function-Based Object Recognition System , 1993, Fuzzy Logic in Artificial Intelligence.

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

[12]  Kevin W. Bowyer,et al.  Generic Recognition of Articulated Objects through Reasoning about Potential Function , 1995, Comput. Vis. Image Underst..

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

[14]  Dmitry B. Goldgof,et al.  Building a B-rep from a segmented range image , 1994, Proceedings of 1994 IEEE 2nd CAD-Based Vision Workshop.

[15]  Lawrence O. Hall,et al.  Methods for Combination of Evidence in Function-Based 3-D Object Recognition , 1993, Int. J. Pattern Recognit. Artif. Intell..

[16]  Alex Pentland,et al.  Good vibrations: modal dynamics for graphics and animation , 1989, SIGGRAPH.

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

[18]  Dmitry B. Goldgof,et al.  Function-based recognition from incomplete knowledge of shape , 1993 .

[19]  Lawrence O. Hall,et al.  Investigation of methods of combining functional evidence for 3-D object recognition , 1991, Other Conferences.

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

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

[22]  Lawrence Birnbaum,et al.  Causal Scene Understanding , 1995, Comput. Vis. Image Underst..

[23]  L. Stark,et al.  Dissertation Abstract , 1994, Journal of Cognitive Education and Psychology.

[24]  Kevin W. Bowyer,et al.  Function-based generic recognition for multiple object categories , 1994 .

[25]  Kevin W. Bowyer,et al.  Generic recognition of articulated objects by reasoning about functionality , 1994, ICPR.