Performance study of several global thresholding techniques for segmentation
Computer Vision, Graphics, and Image Processing
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Approach to Statistical Lip Modelling for Speaker Identification via Chromatic Feature Extraction
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
Segmentation of color lip images by spatial fuzzy clustering
IEEE Transactions on Fuzzy Systems
A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lip image segmentation using fuzzy clustering incorporating an elliptic shape function
IEEE Transactions on Image Processing
Automatic head and facial feature extraction based on geometry variations
Computer-Aided Design
GPU accelerated image processing for lip segmentation
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
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Robust and accurate lip region segmentation is of vital importance for lip image analysis. However, most of the current techniques break down in the presence of mustaches and beards. With mustaches and beards, the background region becomes complex and inhomogeneous. We propose in this paper a novel multi-class, shape-guided FCM (MS-FCM) clustering algorithm to solve this problem. For this new approach, one cluster is set for the object, i.e. the lip region, and a combination of multiple clusters for the background which generally includes the skin region, lip shadow or beards. The proper number of background clusters is derived automatically which maximizes a cluster validity index. A spatial penalty term considering the spatial location information is introduced and incorporated into the objective function such that pixels having similar color but located in different regions can be differentiated. This facilitates the separation of lip and background pixels that otherwise are inseparable due to the similarity in color. Experimental results show that the proposed algorithm provides accurate lip-background partition even for the images with complex background features like mustaches and beards.