Affine invariant model-based object recognition

New techniques are described for model-based recognition of the objects in 3-D space. The recognition is performed from single gray-scale images taken from unknown viewpoints. The objects in the scene may be overlapping and partially occluded. An efficient matching algorithm, which assumes affine approximation to the prospective viewing transformation, is proposed. The algorithm has an offline model preprocessing (shape representation) phase which is independent of the scene information and a recognition phase based on efficient indexing. It has a straightforward parallel implementation. The algorithm was successfully tested in recognition of industrial objects appearing in composite occluded scenes. >

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

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

[3]  Hans P. Morevec.  Towards automatic visual obstacle avoidance , 1977, IJCAI 1977.

[4]  William B. Thompson,et al.  Disparity Analysis of Images , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Robert C. Bolles,et al.  Locating Partially Visible Objects: The Local Feature Focus Method , 1980, AAAI.

[6]  Gene H. Golub,et al.  Matrix computations , 1983 .

[7]  Robert C. Bolles,et al.  3DPO: A Three- Dimensional Part Orientation System , 1986, IJCAI.

[8]  W. Grimson,et al.  Model-Based Recognition and Localization from Sparse Range or Tactile Data , 1984 .

[9]  Richard A. Volz,et al.  Recognizing Partially Occluded Parts , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[11]  John A. Orr,et al.  Applications of Tensor Theory to Object Recognition and Orientation Determination , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[13]  W. Eric L. Grimson,et al.  The combinatorics of local constraints in model-based recognition and localization from sparse data , 1984, JACM.

[14]  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.

[15]  Berthold K. P. Horn.  Robot vision , 1986, MIT electrical engineering and computer science series.

[16]  Edith Schonberg,et al.  Two-Dimensional, Model-Based, Boundary Matching Using Footprints , 1986 .

[17]  Rui J. P. de Figueiredo,et al.  A general moment-invariants/attributed-graph method for three-dimensional object recognition from a single image , 1986, IEEE J. Robotics Autom..

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

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

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

[21]  Micha Sharir,et al.  Identification of Partially Obscured Objects in Two and Three Dimensions by Matching Noisy Characteristic Curves , 1987 .

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

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

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

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