Advances in Visual Computing: 11th International Symposium, by George Bebis, Richard Boyle, Bahram Parvin, Darko Koracin,

By George Bebis, Richard Boyle, Bahram Parvin, Darko Koracin, Ioannis Pavlidis, Rogerio Feris, Tim McGraw, Mark Elendt, Regis Kopper, Eric Ragan, Zhao Ye, Gunther Weber

The quantity set LNCS 9474 and LNCS 9475 constitutes the refereed complaints of the eleventh foreign Symposium on visible Computing, ISVC 2015, held in Las Vegas, NV, united states in December 2015.

The one hundred fifteen revised complete papers and 35 poster papers offered during this ebook have been conscientiously reviewed and chosen from 260 submissions. The papers are equipped in topical sections: half I (LNCS 9474) includes computational bioimaging; special effects; movement and monitoring; segmentation; popularity; visualization; mapping; modeling and floor reconstruction; advancing autonomy for aerial robotics; scientific imaging; digital truth; looking at people; spectral imaging and processing; clever transportation platforms; visible conception and robot platforms. half II (LNCS 9475): purposes; 3D computing device imaginative and prescient; special effects; segmentation; biometrics; development acceptance; acceptance; and digital reality.

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Extra resources for Advances in Visual Computing: 11th International Symposium, ISVC 2015, Las Vegas, NV, USA, December 14-16, 2015, Proceedings, Part II (Lecture Notes in Computer Science)

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Hirokatsu Kataoka, Yoshimitsu Aoki, Kenji Iwata, and Yutaka Satoh 634 Analyzing Activities in Videos Using Latent Dirichlet Allocation and Granger Causality . . . . . . . . . . . . . . . . . . . . Dalwinder Kular and Eraldo Ribeiro 647 Statistical Adaptive Metric Learning for Action Feature Set Recognition in the Wild. . . . . . . . . . . . . . . . . . . . . . . . Shuanglu Dai and Hong Man 657 ST: Spectral Imaging Processing Learning Discriminative Spectral Bands for Material Classification .

4) to be 4/7. Quantitative Analysis: Our proposed approach is evaluated on a variety of natural images and we compare the generated SR results with recent state-ofthe-art methods [9,15,19,21,29] quantitatively measured in PSNR. We use the source code [19,21,29] and executable file [9] provided by the authors or a thirdparty implementation [15] to generate the corresponding HR images based on the same LR input images. To be more specific, given a GT image, the LR image is obtained by performing the bicubic downsampling.

Misawa et al. Fig. 8. 1 SD from the AMM). Fig. 9. 6 SD from the AMM). Table 2. Confusion matrix in detection of habits. Gold standard Proposed system habit non-habit habit 63 12 non-habit 14 to detect human habits. However, we recognize that several issues need to be addressed to make our system practical. In this experiment, we detected only uncommon motions as habits, focused only on the speed of the motion, and employed a subjective gold standard. We will investigate these issues and develop an improved system in the near future.

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