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 publication constitutes the refereed court cases of the 3rd Pacific Rim Symposium on snapshot 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 gains eight significant issues together with all facets of picture and video expertise: picture sensors and multimedia undefined; snap shots and visualization; snapshot and video research; reputation and retrieval; multi-view imaging and processing; laptop imaginative and prescient functions; video communications and networking; and multimedia processing. The papers are equipped in topical sections on faces and pedestrians; panoramic pictures; neighborhood photo research; association and grouping; multiview geometry; detection and monitoring; computational images and forgeries; coding and steganography; popularity and seek; and reconstruction and visualization.

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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

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37–47, 2009. c Springer-Verlag Berlin Heidelberg 2009 38 T. Watanabe, S. Ito, and K. Yokoi (a) Single (b) Paired Fig. 1. Vocabulary of gradient orientations. Though (a) a single gradient orientation has only eight varieties, (b) a pair of them has many more varieties than the single one. 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].

Huang, and S. ): PSIVT 2009, LNCS 5414, pp. 25–36, 2009. c Springer-Verlag Berlin Heidelberg 2009 26 Y. Tsuduki and H. Fujiyoshi Fig. 1. Examples of SIFT keypoint matching. (a) Vodeo of vehicle passing from left to right; (b) Video of pedestrians walking in different directions. We have developed a new approach to keypoint tracking using the SIFT technique. In our approach, we use mean-shift searching to track a keypoint based on the information obtained from the SIFT technique. The mean-shift algorithm [5,6] locates the nearest mode of a point sample distribution [7,8].

Moreover, the visualization method of the feature point tracking result was shown as an example of the tracking of a pedestrian. In the future, we intend to develop a method to automatically detect movement in different directions from a regular flow in order to detect unusual events. References 1. : Distinctive image features from scale-invariant keypoints. Int. Journal of Computer Vision (2004) 2. : A performance evaluation of local descriptors. In: Proceedings of Computer Vision and Pattern Recognition (2003) 3.

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