Probabilistic Solution of Inverse Problems.

Abstract

In this thesis we study the general problem of reconstructing a function, defined on a finite lattice, from a set of incomplete, noisy and/or ambiguous observations. The goal of this work is to demonstrate the generality and practical value of a probabilistic (in particular, Bayesian) approach to this problem, particularly in the context of Computer Vision. In this approach, the prior knowledge about the solution is expressed in the form of a Gibbsian probability distribution on the space of all possible functions, so that the reconstruction task in formulated as an estimation problem. Keywords: Inverse problems; Computer vision; Surface interpolation; Image restoration; Markov random fields; Optimal estimation; Simulated annealing.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1985
Accession Number
ADA161130

Entities

People

  • Jose L. Marroquin

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Bayesian Networks
  • Computational Science
  • Computer Programming
  • Computer Vision
  • Computers
  • Differential Equations
  • Estimators
  • Information Processing
  • Mathematical Filters
  • Neural Networks
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Three Dimensional
  • Two Dimensional

Fields of Study

  • Mathematics

Readers

  • Linear Algebra
  • Neural Network Machine Learning.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Space
  • Space - Space Objects