Unrestricted Recognition of 3D Objects for Robotics Using Multilevel Triplet Invariants

A method for unrestricted recognition of three-dimensional objects was developed. By unrestricted, we imply that the recognition will be done independently of object position, scale, orientation, and pose against a structured background. It does not assume any preceding segmentation or allow a reasonable degree of occlusion. The method uses a hierarchy of triplet feature invariants, which are at each level defined by a learning procedure. In the feedback learning procedure, percepts are mapped on system states corresponding to manipulation parameters of the object. The method uses a learning architecture with channel information representation. This article discusses how objects can be represented. We propose a structure to deal with object and contextual properties in a transparent manner.

[1]  Björn Johansson,et al.  Fast Selective Detection of Rotational Symmetries Using Normalized Inhibition , 2000, ECCV.

[2]  Hiroshi Murase,et al.  Visual learning and recognition of 3-d objects from appearance , 2005, International Journal of Computer Vision.

[3]  David G. Lowe,et al.  Local feature view clustering for 3D object recognition , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[4]  Ronen Basri,et al.  Recognition by Linear Combinations of Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Ramesh C. Jain,et al.  Three-dimensional object recognition , 1985, CSUR.

[6]  Isaac Weiss,et al.  Model-Based Recognition of 3D Objects from Single Images , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

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

[8]  Cordelia Schmid,et al.  Evaluation of Interest Point Detectors , 2000, International Journal of Computer Vision.

[9]  Andrea J. van Doorn,et al.  Invariant Properties of the Motion Parallax Field due to the Movement of Rigid Bodies Relative to an Observer , 1975 .

[10]  C. Schmid,et al.  Indexing based on scale invariant interest points , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[11]  Tomaso Poggio,et al.  Image Representations for Visual Learning , 1996, Science.

[12]  Mohammed Bennamoun,et al.  Representation and Recognition of 3D Free-Form Objects , 2002, Digit. Signal Process..

[13]  Cordelia Schmid,et al.  Local Grayvalue Invariants for Image Retrieval , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Harry Wechsler,et al.  A paradigm for invariant object recognition of brightness, optical flow and binocular disparity images , 1982, Pattern Recognit. Lett..

[15]  Hiroshi Murase,et al.  Learning and recognition of 3D objects from appearance , 1993, [1993] Proceedings IEEE Workshop on Qualitative Vision.

[16]  Jiri Matas,et al.  Object Recognition using the Invariant Pixel-Set Signature , 2000, BMVC.

[17]  Marcus Isaksson.  Face Detection and Pose Estimation using Triplet Invariants , 2002 .

[18]  Gösta H. Granlund,et al.  An Associative Perception-Action Structure Using a Localized Space Variant Information Representation , 2000, AFPAC.

[19]  Gösta H. Granlund.  Does Vision Inevitably Have to be Active , 1998 .

[20]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[21]  J. Fodor,et al.  Connectionism and cognitive architecture: A critical analysis , 1988, Cognition.

[22]  T. Poggio,et al.  A network that learns to recognize three-dimensional objects , 1990, Nature.

[23]  K. Kanatani.  Camera rotation invariance of image characteristics , 1987 .

[24]  Michael Felsberg,et al.  B-spline channel smoothing for robust estimation , 2004 .

[25]  William Grimson,et al.  Object recognition by computer - the role of geometric constraints , 1991 .

[26]  Gösta H. Granlund,et al.  The complexity of vision , 1999, Signal Process..

[27]  Heinrich Niemann,et al.  Neural networks for the recognition and pose estimation of 3D objects from a single 2D perspective view , 2001, Image Vis. Comput..

[28]  Andrew Zisserman,et al.  Geometric invariance in computer vision , 1992 .

[29]  Tomaso Poggio,et al.  Computational Models of Object Recognition in Cortex: A Review , 2000 .

[30]  Alex Pentland,et al.  Probabilistic object recognition and localization , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[31]  Linda G. Shapiro,et al.  Triplet-based object recognition using synthetic and real probability models , 1996, Proceedings of 13th International Conference on Pattern Recognition.