Sparse representations for image decomposition with occlusions
- 1 January 1996
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 10636919,p. 7-12
- https://doi.org/10.1109/cvpr.1996.517046
Abstract
We study the problem of how to detect "interesting objects" appeared in a given image, I. Our approach is to treat it as a function approximation problem based on an over-redundant basis, and also account for occlusions, where the basis superposition principle is no longer valid. Since the basis (a library of image templates) is over-redundant, there are infinitely many ways to decompose I. We are motivated to select a sparse/compact representation of I, and to account for occlusions and noise. We then study a greedy and iterative "weighted L/sup p/ Matching Pursuit" strategy, with O<p<1. We use an L/sup p/ result to compute a solution, select the best template, at each stage of the pursuit.Keywords
This publication has 13 references indexed in Scilit:
- Image recognition with occlusionsPublished by Springer Nature ,1996
- Matching pursuits with time-frequency dictionariesIEEE Transactions on Signal Processing, 1993
- Entropy-based algorithms for best basis selectionIEEE Transactions on Information Theory, 1992
- Eigenfaces for RecognitionJournal of Cognitive Neuroscience, 1991
- Robust Regression and Outlier DetectionPublished by Wiley ,1987
- On a Conjecture of Huber Concerning the Convergence of Projection Pursuit RegressionThe Annals of Statistics, 1987
- Projection PursuitThe Annals of Statistics, 1985
- Projection Pursuit RegressionJournal of the American Statistical Association, 1981
- Robust StatisticsPublished by Wiley ,1981
- L p -methods for robust regressionBIT Numerical Mathematics, 1974