By Guangyu Zhu, Shuicheng Yan, Tony X. Han, Changsheng Xu (auth.), Kuo-Tien Lee, Wen-Hsiang Tsai, Hong-Yuan Mark Liao, Tsuhan Chen, Jun-Wei Hsieh, Chien-Cheng Tseng (eds.)
This two-volume court cases constitutes the refereed papers of the seventeenth overseas Multimedia Modeling convention, MMM 2011, held in Taipei, Taiwan, in January 2011. The fifty one revised common papers, 25 specific consultation papers, 21 poster consultation papers, and three demo consultation papers, have been rigorously reviewed and chosen from 450 submissions. The papers are equipped in topical sections on audio, photograph video processing, coding and compression; media content material looking and retrieval; multi-camera, multi-view, and 3D structures; multimedia indexing and mining; multimedia content material research; multimedia sign processing and communications; and multimedia functions. The exact consultation papers care for content material research for human-centered multimedia functions; huge scale wealthy media information administration; multimedia knowing for purchaser electronics; snapshot item attractiveness and compression; and interactive snapshot and video search.
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Additional info for Advances in Multimedia Modeling: 17th International Multimedia Modeling Conference, MMM 2011, Taipei, Taiwan, January 5-7, 2011, Proceedings, Part II
The center and the right are average and median image respectively, which are obtained from 5 synthetic low-resolution images. Median image outperforms average one, which is overly smooth. Understanding Video Sequences through Super-Resolution 31 Note that the resulting images shown in Fig. 3, average image is overly smooth comparing with the original high-resolution image while the resulting median-value image preserves edge information as well as removing noise. 2, an appropriate prior distribution, as a regularizer, is crucial to keep the super-resolution image away from infeasible solutions.
Even very small errors in the registration of the low-resolution frames can have negative consequences on the estimation of other super-resolution components. Within many superresolution methods, many authors consider that image registration and super-resolution image estimation as two distinct stages. However, we prefer to consider that image registration should be optimizing at the same time as recovering super-resolution, as these two stages are not truly independent, as the super-resolution problem is akin to a neural network, the components and the parameters of them are cross-connected with each other.
Feature-aware texturing. In: Eurographics Symposium on Rendering (2006) 13. : Seam carving for content-aware image resizing. ACM Transactions on Graphics (2007) 14. : Neural networks and the bias/variance dilemma. Neural. Comput. (1992) 15. : Fscav: fast seam carving for size adaptation of videos. In: ACM Multimedia (2009) 16. : Learning to detect a salient object. In: CVPR (2007) 17. : Warp propagation for video resizing. In: CVPR (2010) 18. : Optimized scale-and-stretch for image resizing. ACM Transactions on Graphics (2008) Understanding Video Sequences through Super-Resolution Yu Peng, Jesse S.