By Maria De Marsico, Michele Nappi, Daniel Riccio (auth.), Toshikazu Wada, Fay Huang, Stephen Lin (eds.)
This booklet constitutes the refereed court cases of the 3rd Pacific Rim Symposium on photograph 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 positive aspects eight significant subject matters together with all points of photograph and video know-how: picture sensors and multimedia undefined; photographs and visualization; snapshot and video research; popularity and retrieval; multi-view imaging and processing; desktop imaginative and prescient functions; video communications and networking; and multimedia processing. The papers are prepared in topical sections on faces and pedestrians; panoramic pictures; 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 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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Extra resources for Advances in Image and Video Technology: Third Pacific Rim Symposium, PSIVT 2009, Tokyo, Japan, January 13-16, 2009. Proceedings
Ito, and K. Yokoi Fig. 4. Co-occurrence matrix of gradient orientations. It calculates sums of all pairs of gradient orientations at a given oﬀset. Fig. 5. Overview of CoHOG calculation from which it builds the histograms. The histogram is referred to as the cooccurrence matrix, hereafter. The co-occurrence matrix expresses the distribution of gradient orientations at a given oﬀset over an image as shown in Fig. 4. The combinations of neighbor gradient orientations can express shapes in detail.
Real-time face recognition using feature combination. In: Third IEEE International Conference on Automatic Face and Gesture Recognition. Proceedings, 14-16 April 1998, pp. 312–317 (1998) 2. : Video-based framework for face recognition in video. In: The 2nd Canadian Conference on Computer and Robot Vision. Proceedings, 9-11 May 2005, pp. 330–338 (2005) 3. : The Pattern Recognition Basis of Artiﬁcial Intelligence, 388 pages. Wiley-IEEE Computer Society Press (1998) 4. : Face recognition using line edge map.
Step2 Mean-Shift in Scale Space Our approach uses a mean-shift procedure to estimate the scale parameter of the keypoint at the location obtained in step1. We create a scale weight-map ω(xi , s), which is a 1D array, using the following equation: ω(x , sSj ) = exp −d(x , sSj )2 2σd2 . (6) This mean-shift in scale space is performed on the 1D array of results to locate the mode, as shown in Figure 3(b). The scale mean-shift vector is then obtained using this equation: ΔS = È È M j=0 Kscale (Sj − 1, σs )ω(x , sSj )Sj M j=0 Kscale (Sj − 1, σs )ω(x , sSj ) , (7) where S is the current scale, and Kscale is a kernel function for scale space given by Kscale (S, σs ) = exp −S 2 2σs2 .