By Maria De Marsico, Michele Nappi, Daniel Riccio (auth.), Toshikazu Wada, Fay Huang, Stephen Lin (eds.)
This ebook 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 positive factors eight significant topics together with all points of photograph and video expertise: photo sensors and multimedia undefined; portraits and visualization; photograph 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 prepared 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; reputation and seek; and reconstruction and visualization.
Read Online 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 resources for Advances in Image and Video Technology: Third Pacific Rim Symposium, PSIVT 2009, Tokyo, Japan, January 13-16, 2009. Proceedings
To make a decision whether a feature is lost or not, we compute the Euclidean distance of the SIFT features at the new location x , and previous location x using equation (2). If the distance is above a given threshold, the keypoint at the new location x is deemed a lost feature point and rejected. Association of Keypoints. As shown in Figure 2, we use the SIFT keypoint detector in parallel with a mean-shift procedure for keypoint tracking in order 30 Y. Tsuduki and H. Fujiyoshi to add new keypoints that belong to any new objects appearing in the image.
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]. Collins  proposed using a method of scale change mean-shift based on color features, and She et al.
Afterwards it returns to a rather balanced trend on set 14, where no particular expression or lighting variations are present. Lighting variations, and more precisely left light in set 5, lower performances of T1 , T3 and T4 subsystems, while the only one to remain more reliable is T2 . Fig. 5 shows some examples of the computation of the subsystems convergence speed (for T2 ) and of the identiﬁcation of equilibrium states with diﬀerent values of μ (T4 ); it is to also to notice a case when all the subsystems are in equilibrium, so that we can speak about system equilibrium.
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