DocumentCode :
3632478
Title :
Dealing with occlusions in the eigenspace approach
Author :
A. Leonardis;H. Bischof
Author_Institution :
Dept. for Pattern Recognition & Image Process., Tech. Univ. Wien, Austria
fYear :
1996
Firstpage :
453
Lastpage :
458
Abstract :
The basic limitations of the current appearance-based matching methods using eigenimages are non-robust estimation of coefficients and inability to cope with problems related to occlusions and segmentation. In this paper we present a new approach which successfully solves these problems. The major novelty of our approach lies in the way how the coefficients of the eigenimages are determined. Instead of computing the coefficients by a projection of the data onto the eigenimages, we extract them by a hypothesize-and-test paradigm using subsets of image points. Competing hypotheses are then subject to a selection procedure based on the Minimum Description Length principle. The approach enables us not only to reject outliers and to deal with occlusions but also to simultaneously use multiple classes of eigenimages.
Keywords :
"Lighting","Robustness","Data mining","Shape","Computer vision","Electric breakdown","Pattern recognition","Image processing","Pattern matching","Image segmentation"
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 1996. Proceedings CVPR ´96, 1996 IEEE Computer Society Conference on
ISSN :
1063-6919
Print_ISBN :
0-8186-7259-5
Type :
conf
DOI :
10.1109/CVPR.1996.517111
Filename :
517111
Link To Document :
بازگشت