Year of Award
Doctor of Philosophy (PhD)
Department of Computer Science.
Computer simulation, Computer vision, Lipreading, Markov random fields, Speech perception
This thesis mainly addresses two issues: 1) to investigate how to perform the lip segmentation without knowing the true number of segments in advance, and 2) to investigate how to select the local optimal observation scale for each structure from the viewpoint of lip segmentation e.ectively. Regarding the .rst issue, two number of prede.ned segments independent lip segmentation methods are proposed. In the .rst one, a multi-layer model is built up, in which each layer corresponds to one segment cluster. Subsequently, a Markov random .eld (MRF) derived from this model is obtained such that the segmentation problem is formulated as a labeling optimization problem under the maximum a posteriori-Markov random .eld (MAP-MRF) framework. Suppose the pre-assigned number of segments may over-estimate the ground truth, whereby leading to the over-segmentation. An iterative algorithm capable of performing segment clusters and over-segmentation elimination simultaneously is presented. Based upon this algorithm, a lip segmentation scheme is proposed, featuring the robust performance to the estimate of the number of segment clusters. In the second method, a fuzzy clustering objective function which is a variant of the partition entropy (PE) and implemented using Havrda-Charvat’s structural a-entropy is presented. This objective function features that the coincident cluster centroids in pattern space can be equivalently substituted by one centroid with the function value unchanged. The minimum of the proposed objective function can be reached provided that: (1) the number of positions occupied by cluster centroids in pattern space is equal to the truth cluster number, and (2) these positions are coincident with the optimal cluster centroids obtained under PE criterion. In the implementation, the clusters provided that the number of clusters is greater than or equal to the ground truth are randomly initialized. Then, an iterative algorithm is utilized to minimize the proposed objective function. The initial over-partition will be gradually faded out with the redundant centroids superposed over the convergence of the algorithm. For the second issue, an MRF based method with taking local scale variation into account to deal with the lip segmentation problem is proposed. Supposing each pixel of the target image has an optimal local scale from the segmentation viewpoint, the lip segmentation problem can be treated as a combination of observation scale selection and observed data classi.cation. Accordingly, a multi-scale MRF model is proposed to represent the membership map of each input pixel to a speci.c segment and local-scale map simultaneously. The optimal scale map and the corresponding segmentation result are obtained by minimizing the objective function via an iterative algorithm. Finally, based upon the three proposed methods, some lip segmentation experiments are conducted, respectively. The results show the e.cacy of the proposed methods in comparison with the existing counterparts.
Li, Meng, "On study of lip segmentation in color space" (2014). Open Access Theses and Dissertations. 42.
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