Learning indexing functions for 3-D model-based object recognition

Indexing is an efficient method of recovering match hypotheses in model-based object recognition. Unlike other methods, which search for viewpoint-invariant shape descriptors to use as indices, we use a learning method to model the smooth variation in appearance of local feature sets (LFS). Indexing from LFS effectively deals with the problems of occlusion and missing features. The indexing functions generated by the learning method are probability distributions describing the possible interpretations of each index value. During recognition, this information can be used to select the least ambiguous features for matching. A verification stage follows so that the final reliability and accuracy of the match is greater than that from indexing alone. This approach has the potential to work with a wide range of image features and model types.<<ETX>>

[1]  Jon Louis Bentley,et al.  An Algorithm for Finding Best Matches in Logarithmic Expected Time , 1977, TOMS.

[2]  Ramesh C. Jain,et al.  Recognizing partially visible objects using feature indexed hypotheses , 1986, IEEE J. Robotics Autom..

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

[4]  Andrew M. Wallace.  Matching segmented scenes to models using pairwise relationships between features , 1987, Image Vis. Comput..

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

[6]  David A. Forsyth,et al.  Invariance-a new framework for vision , 1990, [1990] Proceedings Third International Conference on Computer Vision.

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

[8]  Tomaso A. Poggio,et al.  Extensions of a Theory of Networks for Approximation and Learning , 1990, NIPS.

[9]  David W. Jacobs,et al.  Space and Time Bounds on Indexing 3D Models from 2D Images , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  David W. Jacobs.  Space efficient 3-D model indexing , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  David A. Forsyth,et al.  Efficient model library access by projectively invariant indexing functions , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  D. Jacobs.  Space Efficient 3D Model Indexing , 1992 .

[13]  Gérard G. Medioni,et al.  Structural Indexing: Efficient 3-D Object Recognition , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Daphna Weinshall,et al.  3D object recognition by indexing structural invariants from multiple views , 1993, 1993 (4th) International Conference on Computer Vision.

[15]  J.B. Burns,et al.  View Variation of Point-Set and Line-Segment Features , 1993, IEEE Trans. Pattern Anal. Mach. Intell..