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 lawsuits 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 provided in either volumes have been conscientiously reviewed and chosen from 176 submissions. The papers handle matters reminiscent of laptop imaginative and prescient, computer studying, development attractiveness, aim attractiveness, item detection, aim monitoring, picture segmentation, snapshot recovery, face popularity, photograph classification.

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

The volumes CCIS 546 and 547 represent the refereed lawsuits 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 provided in either volumes have been conscientiously reviewed and chosen from 176 submissions. The papers handle matters reminiscent of laptop imaginative and prescient, computer studying, development attractiveness, aim attractiveness, item detection, aim monitoring, picture segmentation, snapshot recovery, face popularity, photograph classification.

Show description

Read or Download Computer Vision: CCF Chinese Conference, CCCV 2015, Xi'an, China, September 18-20, 2015, Proceedings, Part II PDF

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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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Keywords: Stereoscopic video 1 · Retargeting · Super-resolution Introduction Stereoscopic contents, such as still images and videos, extend visual communication to the third dimension by presenting two parallel views of the observed scenery. The fascinating 3D view experience has received much attention and the popularity of 3D entertainment has been significantly increased. In recent years, many researchers have made remarkable progress in 3D capture and display technology. More and more commercial products like 3D cinemas, televisions, smart phones and PDAs have come into our lives.

The purpose of sparse representation is to estimate the main information of the test sample using non-zero coefficient as little as possible. In other words, we need to find the x0 which has less non-zero coefficient and can be a good estimation of y with A . 2 Classification Based on Sparse Representation (SRC) J. Wright et al. introduced the Sparse Representation based Classification (SRC) method which had applied to face recognition and pattern recognition [3]. t Ax  y (4) ||x||0 is the  0 -norm, defined as the number of non-zero entries in the vector x .

N . (6) 36 K. Kang et al. Note the unknown HR frame I, LR observation Yk , as well as noise V are rearranged in column lexicographic order in the pixel domain. Suppose the HR frame’s resolution is rP × rQ and each of the LR frame’s is P × Q, where r is the down-sampling factor, the sizes of I and Yk are (rP × rQ) × 1 and (P × Q) × 1 respectively. The blurring matrix H, (rP × rQ) × (rP × rQ), is used to describe atmospheric, camera lens’, or sensors’ effects. The motion matrix Fk , (rP × rQ) × (rP × rQ), maps reference frame to the k-th frame.

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