By Yo-Sung Ho, Jitao Sang, Yong Man Ro, Junmo Kim, Fei Wu
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 provided in those lawsuits was once conscientiously reviewed and chosen from 224 submissions. The papers have been prepared in topical sections named: photo and audio processing; multimedia content material research; multimedia functions and companies; video coding and processing; multimedia illustration studying; visible realizing and popularity on large information; coding and reconstruction of multimedia facts with spatial-temporal details; 3D image/video processing and purposes; video/image caliber overview and processing; social media computing; human motion attractiveness in social robotics and video surveillance; contemporary advances in image/video processing; new media illustration and transmission applied sciences for rising UHD prone.
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Additional info for Advances in Multimedia Information Processing – PCM 2015: 16th Pacific-Rim Conference on Multimedia Gwangju, South Korea, September 16–18, 2015, Proceedings, Part I
Blur kernel estimation using the radon transform. In: CVPR, pp. 241–248 (2011) 6. : Removing camera shake from a single photograph. ACM Trans. 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.
However, these methods do not guarantee a global optimum and their clustering performance mainly depends on their initializations. To alleviate this initialization problem, this paper proposes a two-step greedy strategy by exploring proper neighbors that span an initial subspace. Firstly, for each data point, we seek a sparse representation with respect to its nearest neighbors. The data points corresponding to nonzero entries in the learning representation form an initial subspace, which potentially rejects bad or redundant data points.
2 (Right), we perform object detection with template matching (NSSD or ZNCC). The result data of matching algorithm provide the location of the corresponding object (CL) in the target image. Other neighbor results and locations (NLs), which are in a limit of a threshold value, can also be found. 2 W. -S. Ho Object Identiﬁcation We apply the result data from the previous subsection to locate the suitable location of interested object (OL) in a full target image (Table 1). Assume: The previous object position (PP) is reserved in a memory buffer.