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Bayesian clustering for unsupervised estimation of surface and texture models

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2 Author(s)
J. F. Silverman ; Lab. for Eng. Man/Machine Syst., Brown Univ., Providence, RI, USA ; D. B. Cooper

A method of calculating the maximum-likelihood clustering for the unsupervised estimation of polynomial models for the data in images of smooth surfaces or for range data for such surfaces is presented. An image or a depth map of a region of smooth 3-D surface is modeled as a polynomial plus white noise. A region of physically meaningful textured-image such as the image of foliage, grass, or road in outdoor scenes or conductor or lintburn on a thick-film substrate is modeled as a colored Gaussian-Markov random field (MRF) with a polynomial mean-value function. Unsupervised-model parameter-estimation is accomplished by determining the segmentation and model parameter values that maximize the likelihood of the data or a more general Bayesian performance functional. Agglomerative clustering is used for this purpose

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:10 ,  Issue: 4 )