Boltzmann machines for depth recovery using a MRF model

Author

Mundkur, P.Y. ; Kapoor, S. ; Desai, U.B.

Author_Institution

Dept. of Electr. Eng., Indian Inst. of Technol., Bombay, India

Volume

3

fYear

1992

fDate

7-11 Jun 1992

Firstpage

260

Abstract

The authors deal with the problem of depth recovery or surface reconstruction from sparse and noisy range data. Based on earlier insights from Markov random field models for such data, a Boltzmann machine is proposed for the parallel computation of the maximum a posteriori (MAP) estimate of the data. A new consensus function is developed to effectively detect discontinuities in highly sparse and noisy images. Interpolation over missing data sites is first done using only local characteristics of the network. Simulation results are also presented

Keywords

Boltzmann machines; Markov processes; computer vision; image reconstruction; learning (artificial intelligence); Boltzmann machines; MRF model; Markov random field models; consensus function; depth recovery; discontinuities; interpolation; local characteristics; maximum a posteriori; noisy range data; simulation; sparse range data; surface reconstruction; Computational modeling; Concurrent computing; Image reconstruction; Iterative algorithms; Markov random fields; Neural networks; Probability distribution; Simulated annealing; Stochastic processes; Surface reconstruction;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1992. IJCNN., International Joint Conference on

Conference_Location

Baltimore, MD

Print_ISBN

0-7803-0559-0

Type

conf

DOI

10.1109/IJCNN.1992.227164

Filename

227164