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 publication constitutes the refereed lawsuits of the 3rd Pacific Rim Symposium on picture 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; pictures and visualization; picture and video research; acceptance and retrieval; multi-view imaging and processing; laptop imaginative and prescient purposes; 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; popularity 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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T. Wada, F. 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].

The final face recognition stage can be avoided for a face image with a low FQR. This saves on the recognition-time expense for an image that is probably unrecognizable. 2. The final face recognition stage can be delayed until a better quality image of that face is captured, thereby improving the probability of success. 3. 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).

Future work would involve testing a face recognition method both with and without FQR assistance in a large high traffic public area, such as a university administration building. We would also like to use many different face recognition methods to prove that an FQR value can be learned for any face recognition method. References 1. : Real-time face recognition using feature combination. In: Third IEEE International Conference on Automatic Face and Gesture Recognition. Proceedings, 14-16 April 1998, pp.

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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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