Probabilistic Indexing for Object Recognition

Recent papers have indicated that indexing is a promising approach to fast model-based object recognition because it allows most of the possible matches between sets of image features and sets of model features to be quickly eliminated from consideration. This correspondence describes a system that is capable of indexing using sets of three points undergoing 3D transformations and projection by taking advantage of the probabilistic peaking effect. To be able to index using sets of three points, we must allow false negatives. These are overcome by ensuring that we examine several correct hypotheses. The use of these techniques to speed up the alignment method is described. Results are given on real and synthetic data. >

[1]  David G. Lowe,et al.  Three-Dimensional Object Recognition from Single Two-Dimensional Images , 1987, Artif. Intell..

[2]  W. Eric L. Grimson,et al.  A Study of Affine Matching With Bounded Sensor Error , 1992, ECCV.

[3]  Clark F. Olson.  Probabilistic indexing: a new method of indexing 3D model data from 2D image data , 1994, Proceedings of 1994 IEEE 2nd CAD-Based Vision Workshop.

[4]  Isaac Weiss.  Noise resistant projective and affine invariants , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[6]  Clark F. Olson.  Fast alignment using probabilistic indexing , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Jezekiel Ben-Arie.  The Probabilistic Peaking Effect of Viewed Angles and Distances with Application to 3-D Object Recognition , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[9]  Clark F. Olson,et al.  Probabilistic Indexing: Recognizing 3D Objects from 2D Images Using , 1993 .

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

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

[12]  W. Eric L. Grimson,et al.  On the sensitivity of geometric hashing , 1990, [1990] Proceedings Third International Conference on Computer Vision.

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

[14]  Thomas O. Binford,et al.  Bayesian inference in model-based machine vision , 1987, Int. J. Approx. Reason..