Geometric PDE's and Invariants for Problems in Visual Control Tracking and Optimization

Abstract

In this proposal, we considered certain invariant flows to treat a number of key issues in controlled active vision, in particular visual tracking. The flows are all derived from some variational principle and so are physically very well justified. In fact, many of the partial differential equations (PDE's) in imaging are based on curvature driven flows from interfacial physics. They have been shown to be useful for a number of applications including crystal growth, flame propagation, and computer vision. We have extensively studied the problems of optimal transport and optical flow for problems in tracking. Optimal transport has appeared in econometrics, fluid dynamics, automatic control, transportation, statistical physics, shape optimization, expert systems, and meteorology. In particular, for the general visual tracking problem in controlled active vision, a robust and reliable object and shape recognition system is of major importance. We have based a new approach to this problem on the theory of optimal mass transport.

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Document Details

Document Type
Technical Report
Publication Date
Jan 03, 2005
Accession Number
ADA428955

Entities

People

  • Allen R. Tannenbaum

Organizations

  • Georgia Tech

Tags

Communities of Interest

  • Autonomy
  • Biomedical
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Aircrafts
  • Applied Mathematics
  • Computational Fluid Dynamics
  • Computational Science
  • Computer Graphics
  • Computer Vision
  • Computers
  • Diagnostic Imaging
  • Differential Equations
  • Equations
  • Fluid Dynamics
  • Geometry
  • Image Processing
  • Information Science
  • Mathematics
  • Pattern Recognition
  • Recognition

Readers

  • Calculus or Mathematical Analysis
  • Operations Research
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms