By Enqing Chen, Yihong Gong, Yun Tie
The two-volume complaints 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 awarded in those court cases used to be conscientiously reviewed and chosen from 202 submissions.
The concentration of the convention used to be as follows in multimedia content material research, multimedia sign processing and communications, and multimedia purposes and services.
Read Online or Download Advances in Multimedia Information Processing - PCM 2016: 17th Pacific-Rim Conference on Multimedia, Xi´ an, China, September 15-16, 2016, Proceedings, Part II 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 II
Some facial animation systems have been proposed and achieved great success in ﬁlm and game production by utilizing special motion-capture equipments, such as camera arrays , structured light projectors , and facial markers . These methods can provide highly reliable facial animation, but the complex equipment makes them less practical for general users. With depth maps and video from Microsofts Kinect camera, Weise et al.  demonstrated impressive facial animation results, but this method can only work in indoor environments.
D (x||y) = x (log x − log y) + (y − x) (2) Note that the learned basis vectors of W reﬂect the repeating patterns of background noises coupled with H as its activation matrix. 2 Updating Rules of RNMF The optimization problem of RNMF can be solved using the multiplicative updating strategy similarly to that in normal NMF. By carefully choosing the step size of the gradient descent method, we can derive the update algorithms and represent them into multiplicative forms as follows: ⎫ ⎧ ⎪ ⎪ ⎪ ⎪ n ⎬ ⎨ n Vi,j Wi,k ← Wi,k · H Hk,j (3) k,j r ⎪ ⎪ ⎪ ⎪ Wi,k Hk,j + Si,j ⎭ j=1 ⎩j=1 k=1 Hk,t ← Hk,t · ⎧ ⎪ ⎪ ⎨ m ⎪ ⎪ ⎩ i=1 r k=1 Vi,t Wi,k Hk,t + Si,t Wi,k ⎫ ⎪ ⎪ ⎬ m ⎪ ⎪ ⎭ i=1 Wi,k (4) Speech Enhancement Using Non-negative Low-Rank Modeling Vi,t + α (Si,t−1 + Si,t+1 ) r Si,t + Si,t ← Si,t · k=1 27 Wi,k Hk,j β + 1 + 2αSi,t (5) where we use the same symbols to notate the elements from their corresponding matrices with the subscript to identify their positions.
In: IEEE International Conference on Acoustics. Speech and Signal Processing (ICASSP), pp. 494–498. IEEE Press, Shanghai (2016) 12. : Robust principle component analysis? J. ACM. 58(3), 1–37 (2011) 13. : Music/voice separation using the similarity matrix. In: 13th International Society for Music Information Retrieval, Porto, Portugal, pp. 583–588 (2013) 32 Y. Li et al. 14. : Speech enhancement under low SNR conditions via noise estimation using sparse and low-rank NMF with KullbackLeibler divergence.
Advances in Multimedia Information Processing - PCM 2016: 17th Pacific-Rim Conference on Multimedia, Xi´ an, China, September 15-16, 2016, Proceedings, Part II by Enqing Chen, Yihong Gong, Yun Tie