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

By Enqing Chen, Yihong Gong, Yun Tie

ISBN-10: 3319488899

ISBN-13: 9783319488899

The two-volume court cases LNCS 9916 and 9917, represent the court cases 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 complaints used to be 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 functions and services.

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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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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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Sampling Extracting high dimensional features Frame(t) Reducing dimension Sparse measurement matrix Extracting lower dimensional features Fig. 1. Samples of compressed sensing According to the compressed sensing tracking algorithm, multi-scale highdimensional image features should first be acquired from positive and negative samples collected in current frame by rectangular filer. Then, these features will be projected to the low-dimensional compressed space from the high-dimensional space. After trained in the low-dimensional compressed space, the Bayes classifier will be finally used to identify the location of the targets in next frame.

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484–498. Springer, Heidelberg (1998). 1007/BFb0054760 Facial Landmark Localization by Part-Aware Deep Convolutional Network 31 6. : 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.

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