Efficient Multiscale Regularization with Applications to the Computation of Optical Flow

Abstract

A new approach to regularization methods for image processing is introduced and developed using as a vehicle the problem of computing dense optical flow fields in an image sequence. Standard formulations of this problem require the computationally intensive solution of an elliptic partial differential equation which arises from the often used "smoothness constraint" type regularization. We utilize the interpretation of the smoothness constraint as a "fractal prior" to motivate regularization based on a recently introduced class of multiscale stochastic models. The solution of the new problem formulation is computed with an efficient multiscale algorithm.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1993
Accession Number
ADA459986

Entities

People

  • Alan S. Willsky
  • Mark R. Luettgen
  • W. C. Karl

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Brownian Motion
  • Computational Complexity
  • Computations
  • Detection
  • Differential Equations
  • Floating Point Operations
  • Flow Fields
  • Image Processing
  • Inverse Problems
  • Kalman Filtering
  • Mathematical Filters
  • Military Research
  • Multiscale Models
  • Partial Differential Equations
  • Probabilistic Models
  • Two Dimensional

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Vision.
  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)