Color representations used in mouth structure segmentation

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Table of contents

1 Introduction 
2 Mouth structure segmentation in images 
2.1 Previous work
2.1.1 Lip segmentation based on pixel color classication
2.1.2 Mouth segmentation derived from contour extraction
2.1.3 Shape or region constrained methods for lip segmentation
2.1.4 Performance measurement in mouth segmentation tasks
2.2 Experimental work ow
2.2.1 Database description
2.2.2 Error, quality and performance measurement
2.2.3 Ground truth establishment
2.3 Summary
3 Pixel color classication for mouth segmentation 
3.1 Color representations for mouth segmentation
3.1.1 Discriminant analysis of commonly-used color representations
3.1.2 Eect of image pre-processing in FLDA
3.1.3 Case study: the normalized a component
3.2 Gaussian mixtures in color distribution modeling
3.2.1 The K-Means algorithm
3.2.2 Gaussian mixture model estimation using Expectation-Maximization
3.2.3 Case study: Color distribution modeling of natural images
3.2.4 Mouth structure segmentation using K-Means and Gaussian mixtures .
3.3 Summary
4 A perceptual approach to segmentation renement 
4.1 Segmentation renement using perceptual arrays
4.2 Special cases and innite behavior of the rener
4.3 Unsupervised natural image segmentation renement
4.3.1 Rener parameter set-up
4.3.2 Pixel color classication tuning
4.4 Mouth structures segmentation renement
4.5 Summary
5 Texture in mouth structure segmentation 
5.1 Low-level texture description
5.2 High-level texture description
5.2.1 Integration scale
5.2.2 Scale based features for image segmentation
5.3 Scale based image ltering for mouth structure classication
5.4 Texture features in mouth structure classication
5.5 Automatic scale-based rener parameter estimation
5.6 Summary
6 An active contour based alternative for RoI clipping 
6.1 Upper lip contour approximation
6.2 Lower lip contour approximation
6.3 Automatic parameter selection
6.4 Tests and results
6.5 Summary
7 Automatic mouth gesture detection 
7.1 Problem statement
7.1.1 Motivation
7.1.2 Previous work
7.1.3 Limitations and constraints
7.2 Acquisition system set up
7.3 Mouth structure segmentation
7.3.1 Pre-processing and initial RoI clipping
7.3.2 Mouth segmentation through pixel classication
7.3.3 Label renement
7.3.4 Texture based mouth/background segmentation
7.3.5 Region trimming using convex hulls
7.4 Mouth gesture classication
7.4.1 Region feature selection
7.4.2 Gesture classication
7.4.3 Gesture detection stabilization
7.5 Summary
8 Conclusion 
9 Open issues and future work