Model-Based Object Recognition by Geometric Hashing

The Geometric Hashing paradigm for model-based recognition of objects in cluttered scenes is discussed. This paradigm enables a unified approach to rigid object recognition under different viewing transformation assumptions both for 2-D and 3-D objects obtained by different sensors, e.g. vision, range, tactile. It is based on an intensive off-line model preprocessing (learning) stage, where model information is indexed into a hash-table using minimal, transformation invariant features. This enables the on-line recognition algorithm to be particularly efficient. The algorithm is straightforwardly parallelizable. Initial experimentation of the technique has led to successful recognition of both 2-D and 3-D objects in cluttered scenes from an arbitrary viewpoint. We, also, compare the Geometric Hashing with the Hough Transform and the alignment techniques. Extensions of the basic paradigm which reduce its worst case recognition complexity are discussed.

[1]  P. Zsombor-Murray,et al.  Elementary Mathematics from an Advanced Standpoint , 1940, Nature.

[2]  Felix Klein.  Elementary Mathematics from an Advanced Standpoint: Geometry , 1941 .

[3]  I. Kuperman.  Approximate linear algebraic equations , 1971 .

[4]  Dana H. Ballard,et al.  Generalizing the Hough transform to detect arbitrary shapes , 1981, Pattern Recognit..

[5]  Toshiyuki Sakai,et al.  Obtaining Surface Orientation from Texels under Perspective Projection , 1981, IJCAI.

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

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

[8]  Charles R. Dyer,et al.  Model-based recognition in robot vision , 1986, CSUR.

[9]  Olivier D. Faugeras,et al.  HYPER: A New Approach for the Recognition and Positioning of Two-Dimensional Objects , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  W. Eric L. Grimson,et al.  Localizing Overlapping Parts by Searching the Interpretation Tree , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  D. W. Thompson,et al.  Three-dimensional model matching from an unconstrained viewpoint , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[12]  George C. Stockman,et al.  Object recognition and localization via pose clustering , 1987, Comput. Vis. Graph. Image Process..

[13]  Yehezkel Lamdan,et al.  Object recognition by affine invariant matching , 2011, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  Yehezkel Lamdan,et al.  On recognition of 3-D objects from 2-D images , 2011, Proceedings. 1988 IEEE International Conference on Robotics and Automation.

[15]  Shimon Ullman,et al.  Aligning A Model To An Image Using Minimal Information , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[16]  Haim J. Wolfson,et al.  An improved model-based matching method using footprints , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.

[17]  Larry S. Davis,et al.  Pose Determination of a Three-Dimensional Object Using Triangle Pairs , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

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

[19]  Y. J. Tejwani,et al.  Robot vision , 1989, IEEE International Symposium on Circuits and Systems,.

[20]  A. J. Heller,et al.  Verification of recognition and alignment hypotheses by means of edge verification statistics , 1989 .

[21]  Gerard Medioni,et al.  Object recognition using geometric hashing on the Connection Machine , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

[22]  W. Eric L. Grimson,et al.  The Combinatorics Of Object Recognition In Cluttered Environments Using Constrained Search , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[23]  Yehezkel Lamdan,et al.  On the error analysis of 'geometric hashing' , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[24]  H. Wolfson,et al.  Efficient detection of three-dimensional structural motifs in biological macromolecules by computer vision techniques. , 1991, Proceedings of the National Academy of Sciences of the United States of America.