Download Image and Video Technology – PSIVT 2013 Workshops: GCCV by Dongwei Liu, Reinhard Klette (auth.), Fay Huang, Akihiro PDF

By Dongwei Liu, Reinhard Klette (auth.), Fay Huang, Akihiro Sugimoto (eds.)

This booklet constitutes the completely refereed post-conference court cases of the 4 workshops on Photographic Aesthetics and Non-Photorealistic Rendering (PAESNPR13), Geometric houses from Incomplete facts (GPID), caliber evaluation and keep an eye on by means of picture and Video research (QACIVA) and Geometric Computation for laptop imaginative and prescient (GCCV2013), held along with the sixth Pacific-Rim Symposium on photograph and Video expertise (PSIVT) in Guanajuato, Mexico in the course of October 28-November 1, 2013. The 38 revised complete papers offered have been conscientiously chosen from quite a few submissions and canopy all points of Imaging and snap shots and Visualization, Image/Video Coding and Transmission; Processing and research; Retrieval and Scene knowing, but in addition functions of snapshot and Video know-how, Biomedical photo Processing and research, Biometrics and photograph Forensics, Computational images and humanities, laptop and robotic imaginative and prescient, trend reputation and Video Surveillance.

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These are images that do not convey much about the scene, and can be said to lack identifiable composition elements such as structure or balance. 5 Conclusion Quantifying image aesthetics is a difficult problem for humans, let alone computers. There is a strong demand for automatic aesthetic prediction systems in the digital imaging industry. To train such systems, we need methods to efficiently collect aes-thetic rating data from a large group of human subjects. We have seen in this paper that a binary rating scale is an effective tool for collecting such data, which is signifi-cant because binary ratings can replace the more difficult task of rating aesthetic content fairly on a numerical scale.

Note that Gaussian kernel can also be used for computing the mean shift. Let the corresponding feature vector x(t + 1) be in the kernel center, and the set of feature vectors xi (t), where i = 1, . . , N , be in the previous frame within the bandwidth hd from ft . For each pixel xd (t + 1) at ft+1 and the pixels within a kernel window at ft , apply the mean-shift filter to find the mode of the region by calculating the mean-shift vector from Eq. (1) repeatedly until it converges, where m(·) < ε (pre-defined threshold).

Association for Computing Machinery’s Special Interest Group on Computer Graphics and Interactive Techniques, pp. 1221–1226 (2006) 6. : Advances in image and video segmentation. sg Abstract. Any system that is able to reliably measure the aesthetic appeal of photographs would be of considerable importance to the digital imaging industry. Researchers have built automated rating systems using machine learning techniques applied to features extracted from images. In this paper, we study the effectiveness of ACQUINE, a comprehensive and publicly available rating system, using data obtained from voters in a crowd sourced manner.

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