• Journal of Korea Multimedia Society
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• v.7 no.11
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• pp.1531-1539
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• 2004

In this paper, we propose an efficient method for locating human lips. Based on Point Distribution Model and Principle Component Analysis, a lip shape model is built. Lip boundary model is represented based on the concatenated gray level distribution model. We calculate the distribution of shape parameters using Gaussian mixture. The problem to locate lip is simplified as the minimization problem of matching object function. The Down Hill Simplex Algorithm is used for the minimization with Gaussian Mixture for setting initial condition and refining estimate of lip shape parameter, which can refrain iteration from converging to local minima. The experiments have been performed for many images, and show very encouraging result.

• Journal of Korea Multimedia Society
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• v.6 no.6
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• pp.968-976
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• 2003

In this paper, we propose a novel method for lip recognition. Lip model is built based on the concatenated gray level distribution model, and the recognition problem is simplified as the minimization problem of matching object function. The Down Hill Simplex Algorithm is used for the minimization with the proposed novel method for setting initial condition, which can refrain Iteration from converging to local minima. The proposed algorithm shows extracting lip shape from the test image where Active Shape Model fails.

• Journal of Biomedical Engineering Research
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• v.29 no.3
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• pp.231-238
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• 2008

Nelder-Mead downhill simplex method (DSM), a kind of deterministic optimization algorithms, has been used extensively for magnetoencephalography(MEG) dipolar source localization problems because it dose not require any functional differentiation. Like many other deterministic algorithms, however, it is very sensitive to the choice of initial positions and it can be easily trapped in local optima when being applied to complex inverse problems with multiple simultaneous sources. In this paper, some modifications have been made to make up for DSM's limitations and improve the accuracy of DSM. First of all, initial point determination method for DSM using magnetic fields on the sensor surface was proposed. Secondly, Univariant-DSM combined DSM with univariant method was proposed. To verify the performance of the proposed method, it was applied to simulated MEG data and practical MEG measurements.