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

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

ISBN-10: 3540929568

ISBN-13: 9783540929567

ISBN-10: 3540929576

ISBN-13: 9783540929574

This booklet 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 beneficial properties eight significant issues together with all elements of photo and video know-how: picture sensors and multimedia undefined; snap shots and visualization; photo and video research; reputation and retrieval; multi-view imaging and processing; computing device 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 picture 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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4 Tracking Example of Non-rigid Object Figure 7 shows examples of non-rigid object keypoint tracking using our proposed method. In this video, pedestrians are walking in different directions. Each tracked point expresses the trajectory of the last 50 frames. We can see that our proposed method can obtain a greater number of long trajectories of keypoints than that obtained by KLT. 4 Visualization of Tracked Points This section describes a technique used to visualize a pedestrian flow. The technique uses the result of feature point tracking by the proposed method.

Some multiple-edge-based feature descriptors also have been proposed. Wu et al. proposed edgelet feature descriptor which expresses long curves of edges [14]. 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)”.

2. The first two parts extract feature descriptors from input images, and then the last part classifies and outputs classification results. We propose a high-dimensional feature descriptor in Section 3. Our feature descriptor is effective for classification, because it contains building blocks that have an extensive vocabulary. Co-occurrence Histograms of Oriented Gradients for Pedestrian Detection Compute Compute Input → gradient → co-occurrence image orientations matrices (a) (b) Classify → with linear SVM (c) 39 Human/ → Non-human classification 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.)


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