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<p>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.</p>
surface models; agglomerative clustering; computerised picture processing; computerised pattern recognition; unsupervised parameter estimation; Bayesian clustering; texture models; maximum-likelihood clustering; polynomial models; depth map; Gaussian-Markov random field; segmentation; Bayes methods; computerised pattern recognition; computerised picture processing; Markov processes; polynomials

J. Silverman and D. Cooper, "Bayesian Clustering for Unsupervised Estimation of Surface and Texture Models," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 10, no. , pp. 482-495, 1988.
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