Accession Number:

ADA523707

Title:

Manifold-Based Image Understanding

Descriptive Note:

Final rept. 15 May 2006-30 Sep 2007

Corporate Author:

RICE UNIV HOUSTON TX

Personal Author(s):

Report Date:

2010-06-30

Pagination or Media Count:

22.0

Abstract:

This project aimed toward a unified theory and practical toolset for the analysis and processing of signal and image manifolds for signal and image understanding purposes. The unifying theme was the multiscale geometric structure of signal and image families and manifolds. Specifically, we developed theory and tools for model-based signal and image recognition and registration, sensing and compressing data on manifolds, and data-driven manifold modeling and learning. The results of our work include 1 the smashed filter, a new tool for compressive classification 2 our theoretical proof of the sufficiency of random projections to compressively capture signals on a manifold, with application to the theory of compressive sensing and 3 the development of new theory and algorithms for learning manifold models for signal and image families.

Subject Categories:

  • Information Science
  • Numerical Mathematics
  • Theoretical Mathematics
  • Computer Programming and Software
  • Cybernetics
  • Miscellaneous Detection and Detectors
  • Human Factors Engineering and Man Machine Systems

Distribution Statement:

APPROVED FOR PUBLIC RELEASE