Disparity Estimation Based on Bayesian Maximum A Posteriori (MAP) Algorithm

Sang Hwa LEE; Jong-Il PARK; Seiki INOUE; Choong Woong LEE

Disparity Estimation Based on Bayesian Maximum A Posteriori (MAP) Algorithm

Sang Hwa LEE, Jong-Il PARK, Seiki INOUE, Choong Woong LEE

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In this paper, a general formula of disparity estimation based on Bayesian Maximum A Posteriori (MAP) algorithm is derived and implemented with simplified probabilistic models. The formula is the generalized probabilistic diffusion equation based on Bayesian model, and can be implemented into some different forms corresponding to the probabilistic models in the disparity neighborhood system or configuration. The probabilistic models are independence and similarity among the neighboring disparities in the configuration. The independence probabilistic model guarantees the discontinuity at the object boundary region, and the similarity model does the continuity or the high correlation of the disparity distribution. According to the experimental results, the proposed algorithm had good estimation performance. This result showes that the derived formula generalizes the probabilistic diffusion based on Bayesian MAP algorithm for disparity estimation. Also, the proposed probabilistic models are reasonable and approximate the pure joint probability distribution very well with decreasing the computations to O(n()) from O(n()⁴) of the generalized formula.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E82-A No.7 pp.1367-1376

Publication Date: 1999/07/25

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Category: Image Theory

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Sang Hwa LEE, Jong-Il PARK, Seiki INOUE, Choong Woong LEE, "Disparity Estimation Based on Bayesian Maximum A Posteriori (MAP) Algorithm" in IEICE TRANSACTIONS on Fundamentals, vol. E82-A, no. 7, pp. 1367-1376, July 1999, doi: .
Abstract: In this paper, a general formula of disparity estimation based on Bayesian Maximum A Posteriori (MAP) algorithm is derived and implemented with simplified probabilistic models. The formula is the generalized probabilistic diffusion equation based on Bayesian model, and can be implemented into some different forms corresponding to the probabilistic models in the disparity neighborhood system or configuration. The probabilistic models are independence and similarity among the neighboring disparities in the configuration. The independence probabilistic model guarantees the discontinuity at the object boundary region, and the similarity model does the continuity or the high correlation of the disparity distribution. According to the experimental results, the proposed algorithm had good estimation performance. This result showes that the derived formula generalizes the probabilistic diffusion based on Bayesian MAP algorithm for disparity estimation. Also, the proposed probabilistic models are reasonable and approximate the pure joint probability distribution very well with decreasing the computations to O(n()) from O(n()⁴) of the generalized formula.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e82-a_7_1367/_p

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@ARTICLE{e82-a_7_1367,
author={Sang Hwa LEE, Jong-Il PARK, Seiki INOUE, Choong Woong LEE, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Disparity Estimation Based on Bayesian Maximum A Posteriori (MAP) Algorithm},
year={1999},
volume={E82-A},
number={7},
pages={1367-1376},
abstract={In this paper, a general formula of disparity estimation based on Bayesian Maximum A Posteriori (MAP) algorithm is derived and implemented with simplified probabilistic models. The formula is the generalized probabilistic diffusion equation based on Bayesian model, and can be implemented into some different forms corresponding to the probabilistic models in the disparity neighborhood system or configuration. The probabilistic models are independence and similarity among the neighboring disparities in the configuration. The independence probabilistic model guarantees the discontinuity at the object boundary region, and the similarity model does the continuity or the high correlation of the disparity distribution. According to the experimental results, the proposed algorithm had good estimation performance. This result showes that the derived formula generalizes the probabilistic diffusion based on Bayesian MAP algorithm for disparity estimation. Also, the proposed probabilistic models are reasonable and approximate the pure joint probability distribution very well with decreasing the computations to O(n()) from O(n()⁴) of the generalized formula.},
keywords={},
doi={},
ISSN={},
month={July},}

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TY - JOUR
TI - Disparity Estimation Based on Bayesian Maximum A Posteriori (MAP) Algorithm
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1367
EP - 1376
AU - Sang Hwa LEE
AU - Jong-Il PARK
AU - Seiki INOUE
AU - Choong Woong LEE
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E82-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 1999
AB - In this paper, a general formula of disparity estimation based on Bayesian Maximum A Posteriori (MAP) algorithm is derived and implemented with simplified probabilistic models. The formula is the generalized probabilistic diffusion equation based on Bayesian model, and can be implemented into some different forms corresponding to the probabilistic models in the disparity neighborhood system or configuration. The probabilistic models are independence and similarity among the neighboring disparities in the configuration. The independence probabilistic model guarantees the discontinuity at the object boundary region, and the similarity model does the continuity or the high correlation of the disparity distribution. According to the experimental results, the proposed algorithm had good estimation performance. This result showes that the derived formula generalizes the probabilistic diffusion based on Bayesian MAP algorithm for disparity estimation. Also, the proposed probabilistic models are reasonable and approximate the pure joint probability distribution very well with decreasing the computations to O(n()) from O(n()⁴) of the generalized formula.
ER -