Sparse representations for image decomposition with occlusions

Mike Donahue, Davi Geiger, Robert Hummel, Tyng Luh Liu

Research output: Contribution to journalConference article

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problem based on an over-redundant basis, and also account for occlusions, where the basis superposition principle is no longer valid.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 Lp Matching Pursuit' strategy, with 0 < p < 1. We use an Lp result to compute a solution, select the best template, at each stage of the pursuit.

Original languageEnglish (US)
Pages (from-to)7-12
Number of pages6
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
StatePublished - Jan 1 1996
EventProceedings of the 1996 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - San Francisco, CA, USA
Duration: Jun 18 1996Jun 20 1996

    Fingerprint

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Cite this