Computer Vision: CCF Chinese Conference, CCCV 2015, Xi'an, by Hongbin Zha, Xilin Chen, Liang Wang, Qiguang Miao

By Hongbin Zha, Xilin Chen, Liang Wang, Qiguang Miao

The volumes CCIS 546 and 547 represent the refereed complaints of the CCF chinese language convention on desktop imaginative and prescient, CCCV 2015, held in Xi'an, China, in September 2015.

The overall of 89 revised complete papers offered in either volumes have been rigorously reviewed and chosen from 176 submissions. The papers deal with matters comparable to desktop imaginative and prescient, laptop studying, development acceptance, aim reputation, item detection, objective monitoring, snapshot segmentation, snapshot recovery, face popularity, photo classification.

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Extra resources for Computer Vision: CCF Chinese Conference, CCCV 2015, Xi'an, China, September 18-20, 2015, Proceedings, Part II

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I  i d rij  indicates the difference of the region ri and the surroundings in the CIELab. People easily perceive the colors in the CIELab space, because it makes up for the uneven distribution of the RGB color model. The region is more salient, if the value is larger than others. d r   c  c , where c i , c are the average color value of the region ri and r j respectively. 3 i j j Color Spatial Distribution The experience shows the elements are more salient when they are located in a stationary image region rather than evenly distributed over the whole image.

In terms of video super-resolution, Liu et al. [12] use Bayesian theory to devise the state of the art approach to video super-resolution by estimating the underlying motion, blur kernel and noise level simultaneously. Recent years, many researchers have shifted their focus to mixresolution image or video super-resolution, they utilize high-frequency information of the full-resolution view to up-sample the corresponding low-resolution view according to the correspondences indicated by the associated disparity map [5,22–24].

Soc. Am. A Opt. Image Sci. Vis. 23(10), 2462–2470 (2006) 3. , Perona. : Is Bottom-Up Attention Useful for Object Recognition? In: IEEE Conf. Computer Vision and Pattern Recognition, USA, pp. 37–44 (2004) 4. : Sketch2photo: Internet Image Montage. ACM Trans. Graph 28(5), 1–10 (2009) 5. : Context-Aware Saliency Detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 1915–1926 (2012) 6. : The JPEG2000 Still Image Coding System: an Overview. IEEE Trans. Consumer Elec. 46(4), 1103–1127 (2002) 7. : A Model of Saliency-Based Visual Attention for Rapid Scene Analysis.

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