Accession Number : ADA195229

Title :   Segmentation of Noisy Images Using Nonstationary Markov Fields.

Descriptive Note : Master's thesis,

Corporate Author : NAVAL POSTGRADUATE SCHOOL MONTEREY CA

Personal Author(s) : Hacipasaoglu, Kani

PDF Url : ADA195229

Report Date : Dec 1987

Pagination or Media Count : 69

Abstract : The purpose of this thesis is to develop an algorithm for segmenting images corrupted by a high level of noise with different characteristics. In particular the images considered are composed of several regions describing different objects and background. The algorithm described is based on a Markov Random Field model of the image and uses Kalman Filtering techniques and Dynamic Programming in order to smooth within the regions. The theoretical background for one dimensional and two dimensional data which have different characteristics and simulation results are presented, with examples of synthetic data and underwater images. Keywords: Markov Random field; Dynamic programming; Kalman filtering techniques.

Descriptors :   *KALMAN FILTERING, *IMAGE PROCESSING, *NOISE REDUCTION, ALGORITHMS, DYNAMIC PROGRAMMING, IMAGES, INTENSITY, MARKOV PROCESSES, MATHEMATICAL MODELS, RANDOM VARIABLES, SEGMENTED, SIMULATION, THEORY, UNDERWATER, THESES

Subject Categories : Operations Research
      Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE