By Enqing Chen, Yihong Gong, Yun Tie
The two-volume court cases LNCS 9916 and 9917, represent the complaints of the 17th Pacific-Rim convention on Multimedia, PCM 2016, held in Xi`an, China, in September 2016.
The overall of 128 papers offered in those court cases was once conscientiously reviewed and chosen from 202 submissions.
The concentration of the convention was once as follows in multimedia content material research, multimedia sign processing and communications, and multimedia purposes and services.
Read or Download Advances in Multimedia Information Processing - PCM 2016: 17th Pacific-Rim Conference on Multimedia, Xi´ an, China, September 15-16, 2016, Proceedings, Part I PDF
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Additional resources for Advances in Multimedia Information Processing - PCM 2016: 17th Pacific-Rim Conference on Multimedia, Xi´ an, China, September 15-16, 2016, Proceedings, Part I
However, in an uncontrolled setting, face is likely to have large out-of-plane tilting, occlusion, illumination and expression variations. Facial landmark localization remains a challenging problem. In general, existing methods to locate the facial landmarks can be divided into three categories: the ﬁrst category is the ASM  and AAM  based methods, which ﬁt a generative model by global facial appearance. However, these methods require expensive iterative steps and rely on good initializations.
Active shape models-their training and application. Comput. Vis. Image Underst. 61(1), 38–59 (1995) 7. : Caﬀe: Convolutional architecture for fast feature embedding. 5093 (2014) 8. : One millisecond face alignment with an ensemble of regression trees. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1867–1874. IEEE (2014) 9. : Interactive facial feature localization. , Schmid, C. ) ECCV 2012. LNCS, vol. 7578, pp. 679–692. Springer, Heidelberg (2012). 1007/978-3-642-33712-3 49 10.
Decision-making model tracking 1: Input: the t frame in the videos 2: if S > θ and τ = 0 then 3: To preserve the relative state of classiﬁer org clf and then collect the positive and negative samples of the initialization classiﬁer new clf around the targets in t-1 frame. 4: To update dictionary D by step 2 and the work out τ 5: To update classiﬁer new clf according to (3) and the deal with the next frame. 6: else if S > θ and τ = −1 then 7: To identify targets using classiﬁer new clf according to (1) in t frame t 8: To update dictionary D by step 2 and the work out τ 9: To update classiﬁer new clf according to (3) and the deal with the next frame.
Advances in Multimedia Information Processing - PCM 2016: 17th Pacific-Rim Conference on Multimedia, Xi´ an, China, September 15-16, 2016, Proceedings, Part I by Enqing Chen, Yihong Gong, Yun Tie
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