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Depth recovery using Markov Random Fields

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Abstract

This paper deals with the problem of depth recovery or surface reconstruction from sparse and noisy range data. The image is modelled as a Markov Random Field and a new potential function is developed to effectively detect discontinuities in highly sparse and noisy images. No use of any line process is made. Interpolation over missing data sites is first done using local characteristics and simulated annealing is then used to compute the maximum a posteriori (map) estimate. Results of software simulations carried out on actual range images along with details of the simulations are presented.

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Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology, 400 076, Powai, Bombay, India

    S Kapoor, P Y Mundkur & U B Desai

Authors
  1. S Kapoor
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  2. P Y Mundkur
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  3. U B Desai
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Additional information

This work was supported by the mhrd grants for Laboratory for Artificial Neural Networks and for Digital Communication and Signal Processing.

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Kapoor, S., Mundkur, P.Y. & Desai, U.B. Depth recovery using Markov Random Fields. Sadhana 18, 17–29 (1993). https://doi.org/10.1007/BF02811384

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  • DOI: https://doi.org/10.1007/BF02811384

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