Advances in Image and Video Technology: Third Pacific Rim by Maria De Marsico, Michele Nappi, Daniel Riccio (auth.),

By Maria De Marsico, Michele Nappi, Daniel Riccio (auth.), Toshikazu Wada, Fay Huang, Stephen Lin (eds.)

This ebook constitutes the refereed lawsuits of the 3rd Pacific Rim Symposium on photograph and Video know-how, 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 beneficial properties eight significant issues together with all elements of photo and video expertise: picture sensors and multimedia undefined; portraits and visualization; photo and video research; popularity and retrieval; multi-view imaging and processing; computing device imaginative and prescient purposes; video communications and networking; and multimedia processing. The papers are prepared in topical sections on faces and pedestrians; panoramic photographs; neighborhood photo research; association and grouping; multiview geometry; detection and monitoring; computational images and forgeries; coding and steganography; attractiveness and seek; and reconstruction and visualization.

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Extra resources for Advances in Image and Video Technology: Third Pacific Rim Symposium, PSIVT 2009, Tokyo, Japan, January 13-16, 2009. Proceedings

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Sabzmeydani et al. proposed shapelet feature descriptor based on selected edges by AdaBoost [15]. 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.

It is informative for pedestrian classification. Mathematically, a co-occurrence matrix C is defined over an n × m image I, parameterized by an offset (x, y), as: n m Cx,y (i, j) = p=1 q=1 1, if I(p, q) = i and I(p + x, q + y) = j 0, otherwise. (1) Co-occurrence Histograms of Oriented Gradients for Pedestrian Detection 41 Fig. 6. Offsets of co-occurrence matrices. Offsets are smaller than the large dashedcircle. The center small white-circle and the other 30 dark-circles are paired. We calculate 31 Co-occurrence matrices with different offsets including zero offset.

FQRs could be used to automatically select face recognition thresholds and/or parameters in response to detected FQRs to improve face recognition confidence. 4. While a detected person is still unrecognized, whether in the scene or not, the face recognition system will only need to store a small set of high FQR images for that person (for recognition at a later time). Without FQRs the system would have to store every single image of the person to optimize the chance of recognizing at least one of the input images (an undesired situation especially with many camera’s, thousands of frames, and hundreds of people moving quickly through a scene).

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