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Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/jspui/handle/100/202

Title: Depth and image recovery using a mrf model
Authors: KAPOOR, S
MUNDKUR, PY
DESAI, UB
Keywords: depth recovery
markov random field
clique model
simulated annealing
surface reconstruction
early vision
heuristic recovery methods
Issue Date: 1994
Publisher: IEEE COMPUTER SOC
Citation: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,16(11)1117-1122
Abstract: This paper deals with the problem of depth recovery and image restoration from sparse and noisy image data. The image is modeled as a Markov Random Field and a new energy function is developed to effectively detect discontinuities in highly sparse and noisy images. The model provides an alternative to the use of a line process. Interpolation over missing data sites is first done using local characteristics to obtain initial estimates and then simulated annealing is used to compute the Maximum a posteriori (MAP) estimate. A threshold on energy reduction per iteration is used to speed up simulated annealing by avoiding computation that contributes little to the energy minimization. Moreover, a minor modification of the posterior energy function gives improved results for random as well as structured sparsing problems. Results of simulations carried out on real range and intensity images along with details of the simulations are presented.
URI: http://dx.doi.org/10.1109/34.334392
http://dspace.library.iitb.ac.in/xmlui/handle/10054/13992
http://hdl.handle.net/100/202
ISSN: 0162-8828
Appears in Collections:Letter

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