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 court cases LNCS 9314 and 9315, represent the lawsuits 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 offered in those court cases was once conscientiously reviewed and chosen from 224 submissions. The papers have been geared up in topical sections named: photo and audio processing; multimedia content material research; multimedia functions and providers; video coding and processing; multimedia illustration studying; visible knowing and popularity on colossal information; coding and reconstruction of multimedia facts with spatial-temporal info; 3D image/video processing and purposes; video/image caliber overview 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 prone.

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Extra 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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Option 3: (Left) object location, (Right) adaptive template image Table 3. Comparison of the PSNR values Object Searching with Combination of Template Matching (a) (b) (c) (d) (e) (f) 39 Fig. 8. The experiment results: (a) SAD method, (b) NSSD method, (c) ZNCC method, (d) Chantara’s [13] (e) ground truth [14], (f) proposed method, the results are captured in different frames 40 W. -S. Ho Table 4. 77 position of involved object and the updated template. In the third option, the matching method is switched between NSSD and ZNCC algorithms.

To address these two problems, we propose to incorporate face landmark detection with image deblurring, which is elaborated in the following sections. 16 Y. Huang et al. (a) (c) (b) (d) Fig. 3. Illustration of landmark detection. (a) and (c): detected contours using the landmark detector training on clear images and blurred images; (b) and (d): corresponding restored results. 2 Robust Face Landmark Detector Training Accurately locating salient contours of testing face image within short time is a key step.

Graph. 25(3), 787–794 (2006) 7. : Blur-Kernel estimation from spectral irregularities. , Schmid, C. ) ECCV 2012, Part V. LNCS, vol. 7576, pp. 622–635. Springer, Heidelberg (2012) 8. : Deblurring by example using dense correspondence. In: ICCV, pp. 2384–2391 (2013) 9. : Deblurring low-light images with light streaks. In: CVPR, pp. 3382–3389 (2014) 10. : PSF estimation using sharp edge prediction. In: CVPR, pp. 1–8 (2008) 11. : Blind deconvolution using a normalized sparsity measure. In: CVPR, pp.

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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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