Advances in Visual Computing: 9th International Symposium, by Margarida Ruela, Catarina Barata, Jorge S. Marques (auth.),

By Margarida Ruela, Catarina Barata, Jorge S. Marques (auth.), George Bebis, Richard Boyle, Bahram Parvin, Darko Koracin, Baoxin Li, Fatih Porikli, Victor Zordan, James Klosowski, Sabine Coquillart, Xun Luo, Min Chen, David Gotz (eds.)

The quantity units LNCS 8033 and 8034 constitutes the refereed complaints of the ninth foreign Symposium on visible Computing, ISVC 2013, held in Rethymnon, Crete, Greece, in July 2013. The sixty three revised complete papers and 35 poster papers awarded including 32 unique song papers have been rigorously reviewed and chosen from greater than 220 submissions. The papers are geared up in topical sections: half I (LNCS 8033) includes computational bioimaging; special effects; movement, monitoring and popularity; segmentation; visualization; 3D mapping, modeling and floor reconstruction; function extraction, matching and popularity; sparse equipment for machine imaginative and prescient, photos and clinical imaging; and face processing and popularity. half II (LNCS 8034) contains subject matters reminiscent of visualization; visible computing with multimodal facts streams; visible computing in electronic cultural background; clever environments: algorithms and purposes; purposes and digital reality.

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Thirdly, symmetry was evaluated using three color descriptors: the mean color vector (MCV), the uni-dimensional color histogram (UCH) and the generalized color moments (GCM). All descriptors were computed in four different color spaces: RGB, HSV, L*a*b* and the O1/2/3. MCV and UCH were both used to describe a single channel or to describe the three channels altogether. GCM were solely used to describe all three channels. , 45}. Finally, two classifiers were used: k-NN and AdaBoost. , 35}. AdaBoost was used with a decision stump as weak classifier and the single parameter to be tuned was the number of weak classifiers, T, where T ∈ {2, 3, 4, 5}.

6c, there is low contrast between adjacent small bowel segments, but proposed method can still detect desired small bowel segment’s contour correctly. Table 1 shows the improvement of our proposed moving benchmark line method. Compared with manual measurement results, fixed benchmark line method acquires 95%, 91% and 93% accuracy in Sequence1, Sequence2 and Sequence3. Whereas our proposed moving benchmark line method achieves 93%, 96% and 95% accuracy in these sequences, which outperform fixed benchmark line method in two sequences.

Sahar Q. Saleh, Muhammad Hussain, Ghulam Muhammad, and George Bebis 406 416 Table of Contents – Part II XXXIII Energy-Transfer Features for Pedestrian Detection . . . . . . . . . . ˇ Radovan Fusek, Eduard Sojka, Karel Mozdˇreˇ n, and Milan Surkala Basic Shape Classification Using Spatially Normalised Fourier Shape Signature . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chin Yeow Wong, Stephen Ching-Feng Lin, Guannan Jiang, and Ngai Ming Kwok 425 435 Normalized Matting of Interest Region .

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