A Markov Random Field Model-Based Approach to Image Interpretation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Interpretation Using Bayesian Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Integration Scheme for Image Segmentation and Labeling Based on Markov Random Field Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Image interpretation consists of interleaving the low-level task of image segmentation and the high-level task of interpretation. The idea being that the interpretation block guides the segmentation block which in turn helps the interpretation block in better interpretation. In this paper, we develop a joint segmentation and image interpretation scheme using the notion of joint hidden Markov model (HMM) for probabilistic modeling of spatial relationship. We find the optimal interpretation labels, which are nothing but the optimal state sequence of the HMM.