• Journal of KIISE:Computing Practices and Letters
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• v.16 no.8
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• pp.883-887
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• 2010

3D face tracking is one of essential techniques in computer vision such as surveillance, HCI (Human-Computer Interface), Entertainment and etc. However, 3D face tracking demands high computational cost. It is a serious obstacle to applying 3D face tracking to mobile devices which usually have low computing capacity. In this paper, to reduce computational cost of 3D tracking and extend 3D face tracking to mobile devices, an efficient particle filtering method using MLESAC(Maximum Likelihood Estimation SAmple Consensus) motion estimation is proposed. Finally, its speed and performance are evaluated experimentally.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.4 s.316
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• pp.44-50
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• 2007

In this paper, we proposed the algorithm for the extraction of the corresponding points from images. The proposed algorithm EMSAC is based on RANSAC and EM algorithms. In the RANSAC procedure, the N corresponding points are randomly selected from the observed total corresponding points to estimate the homography matrix, H. This procedure continues on its repetition until the optimum H are estimated within number of repetition maximum. Therefore, it takes much time and does not converge sometimes. To overcome the drawbacks, the EM algorithm was used for the selection of N corresponding points. The EM algorithm extracts the corresponding points with the highest probability density to estimate the optimum H. By the experiments, it is demonstrated that the proposed method has exact and fast performance on extraction of corresponding points by combining RANSAC with EM.