Download e-book for iPad: Advances in Multimedia Information Processing – PCM 2015: by Yo-Sung Ho, Jitao Sang, Yong Man Ro, Junmo Kim, Fei Wu

By Yo-Sung Ho, Jitao Sang, Yong Man Ro, Junmo Kim, Fei Wu

ISBN-10: 3319240749

ISBN-13: 9783319240749

ISBN-10: 3319240757

ISBN-13: 9783319240756

The two-volume complaints LNCS 9314 and 9315, represent the court cases of the 16th Pacific-Rim convention on Multimedia, PCM 2015, held in Gwangju, South Korea, in September 2015.

The overall of 138 complete and 32 brief papers awarded in those lawsuits was once rigorously reviewed and chosen from 224 submissions. The papers have been equipped in topical sections named: picture and audio processing; multimedia content material research; multimedia purposes and companies; video coding and processing; multimedia illustration studying; visible knowing and popularity on monstrous facts; coding and reconstruction of multimedia information with spatial-temporal details; 3D image/video processing and purposes; video/image caliber evaluation and processing; social media computing; human motion attractiveness in social robotics and video surveillance; fresh advances in image/video processing; new media illustration and transmission applied sciences for rising UHD companies.

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Read Online or Download Advances in Multimedia Information Processing – PCM 2015: 16th Pacific-Rim Conference on Multimedia Gwangju, South Korea, September 16–18, 2015, Proceedings, Part I PDF

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Additional resources for Advances in Multimedia Information Processing – PCM 2015: 16th Pacific-Rim Conference on Multimedia Gwangju, South Korea, September 16–18, 2015, Proceedings, Part I

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2. Framework of the proposed method. 2 The Proposed Method In this section, we firstly explain the motivation of this work, then give a description of our face deblurring method in detail. As shown in Fig. 2, our method mainly consists of three components: (1) robust face landmark detector training, (2) salient contour detection and (3) blind image deblurring. 1 Motivation As stated previously, most existing face deblurring methods are heavily dependent on exemplar set construction and candidate matching.

14], Zhong et al. [23], and Xu et al. [20]. (a) Real image 1 (b) Pan [1] (c) Ours (d) Real Image 2 (e) Pan [1] (f) Ours Fig. 5. 1 Experiments on Synthesised Dataset and Real Images To validate the effectiveness of our method, experiments are conducted on the synthesised dataset and real images used in [1] with the same settings. Qualitative and qualitative comparison results are provided in Figs. 4 and 5. Due to Efficient Face Image Deblurring 19 space constraint, only restored images of our method and the baseline method are shown in Fig.

1 Experiments on Synthesised Dataset and Real Images To validate the effectiveness of our method, experiments are conducted on the synthesised dataset and real images used in [1] with the same settings. Qualitative and qualitative comparison results are provided in Figs. 4 and 5. Due to Efficient Face Image Deblurring 19 space constraint, only restored images of our method and the baseline method are shown in Fig. 5. As we can see, our overall performance is comparable or even better than those achieved by the best baseline method.

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Advances in Multimedia Information Processing – PCM 2015: 16th Pacific-Rim Conference on Multimedia Gwangju, South Korea, September 16–18, 2015, Proceedings, Part I by Yo-Sung Ho, Jitao Sang, Yong Man Ro, Junmo Kim, Fei Wu


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