Bayesian segmentation of range images of polyhedral objects using entropy-controlled quadratic Markov measure field models
Applied Optics, Vol. 47, Issue 22, pp. 4106-4115 (2008)
http://dx.doi.org/10.1364/AO.47.004106
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Abstract
We present a method based on Bayesian estimation with prior Markov random field models for segmentation of range images of polyhedral objects. This method includes new ways to determine the confidence associated with the information given for every pixel in the image as well as an improved method for localization of the boundaries between regions. The performance of the method compares favorably with other state-of-the-art procedures when evaluated using a standard benchmark.
© 2008 Optical Society of America
OCIS Codes
(100.2960) Image processing : Image analysis
(150.5670) Machine vision : Range finding
(150.1135) Machine vision : Algorithms
ToC Category:
Image Processing
History
Original Manuscript: January 28, 2008
Revised Manuscript: June 10, 2008
Manuscript Accepted: June 17, 2008
Published: July 28, 2008
Citation
Carlos Angulo, Jose L. Marroquin, and Mariano Rivera, "Bayesian segmentation of range images of polyhedral objects using entropy-controlled quadratic Markov measure field models," Appl. Opt. 47, 4106-4115 (2008)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-47-22-4106
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