By Maria De Marsico, Michele Nappi, Daniel Riccio (auth.), Toshikazu Wada, Fay Huang, Stephen Lin (eds.)
This e-book constitutes the refereed complaints 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 gains eight significant topics together with all points of snapshot and video expertise: snapshot sensors and multimedia undefined; portraits and visualization; photograph and video research; popularity 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 photograph research; association and grouping; multiview geometry; detection and monitoring; computational images and forgeries; coding and steganography; popularity 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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Extra info for Advances in Image and Video Technology: Third Pacific Rim Symposium, PSIVT 2009, Tokyo, Japan, January 13-16, 2009. Proceedings
IEEE Transaction on Systems, Man, and CyberneticsPart C: Applications and Reviews 35, 344–356 (2005) 10. : Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Transaction on Pattern Analisys and Machine Intelligence 24, 748–763 (2002) 11. : Face, ear and ﬁngerprint: Designing multibiometric architectures. In: Proceedings of the 14th International Conference on Image Analysis and Processing (ICIAP 2007), pp. 437–442 (2007) Using Face Quality Ratings to Improve Real-Time Face Recognition Karl Axnick, Ray Jarvis, and Kim C.
The mean-shift algorithm is a simple nonparametric method for locating the nearest mode of a sample distribution. It has recently been adopted as an eﬃcient tracking technique. When the mean-shift method is applied to keypoint tracking, the gradient density is formed by the weight ω(xi , s) at each image pixel xi . The core of the mean-shift tracking algorithm is the computation of a keypoint motion vector from a location x to a new location x . Generally, a weight map is determined using a color-based appearance model.
2. Shows the recognition rate of images at varying FQR levels predicted by the trained MLPs Once training is completed the MLP can quickly generate FQRs for any input. A system just needs to feed a target image’s 64 x 64 1D vector into the trained MLP, and then within 100 ms an output is generated that is the target image’s FQR. Fig. 2 shows a clear linear relationship between FQRs and recognition success. In order to generate this result, two separate tests were combined and averaged. The ﬁrst test involved creating 10 separate groups of faces with diﬀerent recognition rates.