Download Advances in Multimedia Information Processing - PCM 2016: by Enqing Chen, Yihong Gong, Yun Tie PDF

By Enqing Chen, Yihong Gong, Yun Tie

The two-volume lawsuits LNCS 9916 and 9917, represent the lawsuits 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 lawsuits was once rigorously 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.

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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 I PDF

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Extra 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

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Accordingly, m-1 increasing ∧ point sets q will be generated in the interval of [0,1]. ∧ q= 2n − 1 , 2m + 1 n ∈ [1, m − 1] (11) By m-1 points, the interval [0,1] is divided into m subintervals increasing in length. In dictionary D, there are m target templates that corresponds to the m subintervals. Every time in updating, there will be a random number r in interval [0,1]. The template corresponding to the interval which r falls into will be eliminated and then a new template will be added to the end of the template set.

It first establishes a dictionary which is made up of m sets of target templates T = {t1 , · · · , tm }, n sets of positive trivial templates I = {i1 , · · · , in } as well as n sets of negative trivial templates −I = {−i1 , · · · , −in } (m << n). In the process of tracking, each potential target y is represented by a linear combination of the sample templates in the dictionary. t. C ≥ 0 e− The (4) is an undetermined system of equations. For a promising candidate target, there are only a few nonzero elements among the coefficient e+ and e− .

We have two concerns in designing bottom sharing convolutional layers. First, all the landmarks can incorporate general characteristics. By sharing input image and several convolutional layers, the context over the face can be utilized to locate each landmark. At the same time, all landmarks are implicitly encoded the geometric constraints. Second, sharing bottom layers makes our model time efficiency. In the early stage of the network, each layer extracts low level features. These features can be shared all across the face.

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