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Download 3D Face Processing (The Kluwer International Series in Video by Ming-Hsuan Yang, Narendra Ahuja PDF

By Ming-Hsuan Yang, Narendra Ahuja

ISBN-10: 0792374096

ISBN-13: 9780792374091

ISBN-10: 0792374363

ISBN-13: 9780792374367

ISBN-10: 1402070217

ISBN-13: 9781402070211

ISBN-10: 1402071027

ISBN-13: 9781402071027

3D Face Processing: Modeling, research and Synthesis will curiosity these operating in face processing for clever human desktop interplay and video surveillance. It features a entire survey on present face processing strategies, that can function a reference for college kids and researchers. It additionally covers in-depth dialogue on face movement research and synthesis algorithms, in order to gain extra complicated graduate scholars and researchers.

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Additional resources for 3D Face Processing (The Kluwer International Series in Video Computing): Modeling, Analysis, and Synthesis

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However, the phoneme recognition rate of linear prediction is low. , 1997] trains hidden Markov models (HMMs) [Rabiner, 1989] to automatically label phonemes in both training audio track and new audio track. It models shortterm mouth co-articulation using triphones. The mouth images for a new audio track are generated by reordering the mouth images in the training footage, which requires a very large database. Video Rewrite is an offline approach and needs large computation resources. Chen and Rao [Chen and Rao, 1998] train HMMs to segment the audio feature vectors of isolated words into state sequences.

Waters and Levergood, 1993] and Hong et al. , 2001a] generated the temporal trajectory using sinusoidal interpolation functions. Pelachaud et al. , 1991] used a look-ahead co-articulation model. Cohen and Massaro [Cohen and Massaro, 1993] adopted Löfqvist gestural production model [Lofqvist, ] as the co-articulation model and interactively designed its explicit form based on observation of real speech. 3 Speech-driven face animation Another type of input for “visual speech” synthesis speech signals.

In medical applications related to facial motion disorder such as facial paralysis, visual cues are important for both diagnosis and treatment. 2. (a): The synthesized face motion. (b): The reconstructed video frame with synthesized face motion. 26L codec. 2001]. Compared to other 3D non-rigid facial motion tracking approaches using single camera, which are cited in Section 1, the features of our tracking system can be summarized as: (1) the deformation space is learned automatically from data such that it avoids manual crafting but still captures the characteristics of real facial motion; (2) it is real-time so that it can be used in real-time human computer interface and coding applications; (3) To reduce “drifting” caused by error accumulation in long-term tracking, it uses templates in both the initial frame and previous frame when estimating the template-matchingbased optical flow (see [Tao and Huang, 1999]); and (4) it is able to recover from temporary loss of tracking by incorporating a template-matching-based face detection module.

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3D Face Processing (The Kluwer International Series in Video Computing): Modeling, Analysis, and Synthesis by Ming-Hsuan Yang, Narendra Ahuja


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