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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Additional info for Image and Video Technology – PSIVT 2013 Workshops: GCCV 2013, GPID 2013, PAESNPR 2013, and QACIVA 2013, Guanajuato, Mexico, October 28-29, 2013, Revised Selected Papers
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.