Curve and surface reconstruction by using sequential Markov random fields (MRFs)

K. Hara, H. B. Zha, T. Nagata

Research output: Contribution to journalConference articlepeer-review


The statistical approach using the coupled Markov random field (MRF) and the maximum a posteriori (MAP) estimate has been proposed in order to satisfy both the preservation of local discontinuities and the smoothing of continuous regions for reconstruction of derivative feature measurements and range data. However, this data reconstruction method has some very difficult problems that it is hard to obtain proper results by finding the global optimal solution correctly. Especially, if the ordinary iteration solutions are used for the noisy and rugged data, is it likely that the convergence happens to be at a local optimal solution depending upon the initial value, and the noise smoothing is insufficient or the edge parts are overslurred. To cope with the difficulties, we propose a recovery method that regards the MAP estimation itself as the basic process and do the computation iteratively while controlling smoothing by changing values of the MRF parameters according to some scheduling. The presented algorithm reduces failures because of the local optimization, and is respected to give better results of reconstruction. The applicability of the method has been verified by several reconstruction experiments.

Original languageEnglish
Pages (from-to)1097-1102
Number of pages6
Journal1995 IEEE International Conference on Systems, Man, and Cybernetics
Publication statusPublished - 1995
Externally publishedYes
EventProceedings of the 1995 IEEE International Conference on Systems, Man and Cybernetics. Part 2 (of 5) - Vancouver, BC, Can
Duration: Oct 22 1995Oct 25 1995

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Hardware and Architecture


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