Dealing with occlusions in the eigenspace approach
- 1 January 1996
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
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
This publication has 11 references indexed in Scilit:
- Learning, positioning, and tracking visual appearancePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Fast template matching based on the normalized correlation by using multiresolution eigenimagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Face recognition from one example viewPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Segmentation of range images as the search for geometric parametric modelsInternational Journal of Computer Vision, 1995
- ExSel++: A general framework to extract parametric modelsPublished by Springer Nature ,1995
- Visual learning and recognition of 3-d objects from appearanceInternational Journal of Computer Vision, 1995
- View-based and modular eigenspaces for face recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1994
- Eigenfaces for RecognitionJournal of Cognitive Neuroscience, 1991
- Robust Regression and Outlier DetectionPublished by Wiley ,1987
- Robust StatisticsPublished by Wiley ,1981