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Lip image segmentation using fuzzy clustering incorporating an elliptic shape function.

Shu-Hung Leung¹, Shi-Lin Wang, Wing-Hong Lau

¹Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China. eeeugshl@cityu.edu.hk

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|September 21, 2004

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A new fuzzy clustering method improves lip image segmentation by considering both color and spatial information. This approach enhances lip shape and region differentiation for better speech recognition and speaker authentication.

Area of Science:

Computer Vision
Image Processing
Pattern Recognition

Background:

Lip image analysis is crucial for speech recognition and speaker authentication.
Accurate lip image segmentation is a key step in lip image analysis.

Purpose of the Study:

To introduce a novel fuzzy clustering method for improved lip image segmentation.
To enhance the differentiation between lip and non-lip regions.

Main Methods:

Developed a new fuzzy clustering method incorporating color and spatial distance.
Introduced a novel dissimilarity measure using an elliptic shape function.
Derived a new iterative algorithm for membership and centroid determination.

Main Results:

Related Experiment Videos

The new method effectively differentiates pixels with similar colors in different spatial locations.
Achieved superior differentiation between lip and non-lip regions compared to existing methods.
Demonstrated better membership distribution and lip shape accuracy.

Conclusions:

The proposed fuzzy clustering method offers significant improvements in lip image segmentation.
This technique enhances the quality of lip image analysis for biometric and speech applications.