Title :
Fast alignment using probabilistic indexing
Author_Institution :
Comput. Sci. Div., Univ. of California, Berkeley, CA, USA
Abstract :
The alignment method is a model-based object recognition technique that determines possible object transformations from three hypothesized matches of model and image points. For images and/or models with many features, the running time of the alignment method can be large. Methods of reducing the number of matches that must be examined are presented. The techniques described are the use of the probabilistic indexing system, and the elimination of groups of model points that produce large errors in the transformation determined by the alignment method. Results are presented which show that it is possible to achieve a speedup of over two orders of magnitude while still finding a correct alignment
Keywords :
image recognition; image sequences; probability; alignment method; hypothesized matches; model-based object recognition; object transformations; probabilistic indexing; running time; Computer science; Image databases; Image recognition; Indexing; Object detection; Object recognition; Probability density function; Reflection; Testing; Tires; Uncertainty;
Conference_Titel :
Computer Vision and Pattern Recognition, 1993. Proceedings CVPR '93., 1993 IEEE Computer Society Conference on
Conference_Location :
New York, NY
Print_ISBN :
0-8186-3880-X
DOI :
10.1109/CVPR.1993.341101