Probabilistic Solution of Ill-Posed Problems in Computational Vision.

Abstract

Computational vision is a set of inverse problems. The authors review standard regularization theory, discuss its limitations, and present new stochastic (in particular, Bayesian) methods for their solution. They derive efficient algorithms and describe parallel implementations on digital parallel SIMD architectures, as well as a new class of parallel hybrid computers. Keywords: Stochastic methods; Artificial intelligence; Problem solving; Probablistic approach. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1987
Accession Number
ADA183807

Entities

People

  • J. Marroquin
  • Sanjoy K. Mitter
  • Tomaso Poggio

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Change Detection
  • Computer Vision
  • Detection
  • Equations
  • Estimators
  • Image Segmentation
  • Information Processing
  • Information Systems
  • Military Research
  • Optimal Estimators
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Statistical Analysis
  • Two Dimensional

Readers

  • Artificial Intelligence
  • Linear Algebra
  • Parallel and Distributed Computing.

Technology Areas

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