Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Minimax entropy principle and its application to texture modeling
Neural Computation
Prior Learning and Gibbs Reaction-Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
International Journal of Computer Vision
Markov random field modeling in image analysis
Markov random field modeling in image analysis
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Tooth Cementum Annulation (TCA) is an age estimation method carried out on thin cross sections of the root of the human tooth. Age is computed by adding the tooth eruption age to the count of annual incremental lines which are called tooth rings and appear in the cementum band. Algorithms to denoise and segment the digital image of the tooth section are considered a crucial step towards computer-assisted TCA. The approach in this paper relies on modelling the images as hidden Markov random fields, where gray values are assumed to be pixelwise conditionally independent and normally distributed, given a hidden random field of labels. These unknown labels have to be estimated to segment the image. To account for long-range dependence among the observed values and for periodicity in the placement of tooth rings, the Gibbsian label distribution is specified by a potential function that incorporates macro-features of the TCA image (a FRAME model). An estimation of the model parameters is made by an EM algorithm exploiting the mean field approximation of the label distribution. Segmentation is based on the predictive distribution of the labels given the observed gray values.