Efficient model library access by projectively invariant indexing functions

Projectively invariant shape descriptors allow fast indexing into model libraries without the need for pose computation or camera calibration. Progress in building a model-based vision system for plane objects that uses algebraic projective invariants is described. A brief account of these descriptors is given, and the recognition system is described, giving examples of the invariant techniques working on real images.<<ETX>>

[1]  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.

[2]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

[5]  Gunilla Borgefors,et al.  Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Aaron Heller,et al.  The evolution and testing of a model-based object recognition system , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[7]  David A. Forsyth,et al.  Invariant Descriptors for 3D Object Recognition and Pose , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Lars Nielsen,et al.  Projective area-invariants as an extension of the cross-ratio , 1991, CVGIP Image Underst..

[9]  David W. Jacobs,et al.  Model group indexing for recognition , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Daniel P. Huttenlocher,et al.  Fast affine point matching: an output-sensitive method , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Peter C. Wayner,et al.  Efficiently using invariant theory for model-based matching , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  David A. Forsyth,et al.  Canonical Frames for Planar Object Recognition , 1992, ECCV.