By Maria De Marsico, Michele Nappi, Daniel Riccio (auth.), Toshikazu Wada, Fay Huang, Stephen Lin (eds.)
This e-book constitutes the refereed lawsuits of the 3rd Pacific Rim Symposium on picture and Video expertise, PSIVT 2008, held in Tokyo, Japan, in January 2009.
The 39 revised complete papers and fifty seven posters have been rigorously reviewed and chosen from 247 submissions. The symposium positive factors eight significant issues together with all features of snapshot and video expertise: photo sensors and multimedia undefined; photos and visualization; photo and video research; popularity and retrieval; multi-view imaging and processing; desktop imaginative and prescient functions; video communications and networking; and multimedia processing. The papers are equipped in topical sections on faces and pedestrians; panoramic photos; neighborhood snapshot research; association and grouping; multiview geometry; detection and monitoring; computational images and forgeries; coding and steganography; reputation and seek; and reconstruction and visualization.
Read or Download Advances in Image and Video Technology: Third Pacific Rim Symposium, PSIVT 2009, Tokyo, Japan, January 13-16, 2009. Proceedings PDF
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Additional info for Advances in Image and Video Technology: Third Pacific Rim Symposium, PSIVT 2009, Tokyo, Japan, January 13-16, 2009. Proceedings
3 Visualization Example Figure 10(a) shows visualization examples of pedestrian ﬂow accumulating tracked points over 1 hour(100,000 frames). The circle in the left a color map of the direction of the movement. From the visualization, we can see that there are a lot of people who were crossing to the left in area A. In area B, we also see that there are two movements in opposite directions. Figure 10(b) shows visualization examples of pedestrian ﬂow for every 2 seconds (60 frames). Since the SIFT feature has a scale parameter, the proposed method can obtain better human shapes than that of the KLT.
Proposed shapelet feature descriptor based on selected edges by AdaBoost . Since shapelets are the combination of edges, they can express more detailed shape information than what SIFT/HOG feature descriptors can. We propose a multiple-gradient-orientation-based feature descriptor named “Co-occurrence Histograms of Oriented Gradients (CoHOG)”. CoHOG is histograms of which a building block is a pair of gradient orientations. Since the pair of gradient orientations has more vocabulary than single one as shown in Fig.
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Advances in Image and Video Technology: Third Pacific Rim Symposium, PSIVT 2009, Tokyo, Japan, January 13-16, 2009. Proceedings by Maria De Marsico, Michele Nappi, Daniel Riccio (auth.), Toshikazu Wada, Fay Huang, Stephen Lin (eds.)