Jong-Kae Fwu

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In recent years, many image segmentation approaches have been based on Markov random fields (MRFs). The main assumption of the MRF approaches is that the class parameters are known or can be obtained from training data. In this paper the authors propose a novel method that relaxes this assumption and allows for simultaneous parameter estimation and vector(More)
easily interpreted. The method is implemented iteratively in stages by using a treestructure (TS) initial-ization scheme [5] and the Iterated Conditional Modes (ICM) method [Z]. In the first stage, it is assumed that in the third three, and so on. After the completion of each stage, the best sequence is selected for that stage stages. If based on the(More)
In this correspondence, the objective is to segment vector images, which are modeled as multivariate finite mixtures. The underlying images are characterized by Markov random fields (MRFs), and the applied segmentation procedure is based on the expectation-maximization (EM) technique. We propose an initialization procedure that does not require any prior(More)
A novel method for edge detection in vector images is proposed that does not require any prior knowledge of the imaged scenes. In the derivation, it is assumed that the observed vector images are realizations of spatially quasistationary processes, and that the vector observations are generated by parametric probability distribution functions of known form(More)
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