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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xu Yanjun Du Limin Hou Ziqiang |
| Copyright Year | 1998 |
| Description | Author affiliation: Inst. of Acoust., Acad. Sinica, Beijing, China (Xu Yanjun) |
| Abstract | Visual feature extraction is one of the most important techniques in audiovisual bimodal speech recognition, and also remains a very challenging area in image understanding. A shiftable multiscale transform is introduced into the construction of an active shape model. It uses the pyramidal data to describe the structure of an image, which is invariant to illumination and perspective variability and thus contributes a lot to the improvement of the robustness of the model. A segmental downhill simplex method is also put forward to improve the minimization procedure of lip localization. It employs a kind of "coarse-to-fine" strategy to speed up the convergence and improve the robustness of lip localization. Experiments support the validity of the new method, and show better robustness and higher efficiency. |
| Starting Page | 1029 |
| Ending Page | 1032 |
| File Size | 282442 |
| Page Count | 4 |
| File Format | |
| ISBN | 0780343255 |
| DOI | 10.1109/ICOSP.1998.770790 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1998-10-12 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Wavelet transforms Active shape model Feature extraction Speech Eigenvalues and eigenfunctions Lighting Noise robustness Deformable models Covariance matrix Acoustic waves |
| Content Type | Text |
| Resource Type | Article |
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