Download Advances in Image and Video Technology: Third Pacific Rim by Maria De Marsico, Michele Nappi, Daniel Riccio (auth.), PDF

By Maria De Marsico, Michele Nappi, Daniel Riccio (auth.), Toshikazu Wada, Fay Huang, Stephen Lin (eds.)

ISBN-10: 3540929568

ISBN-13: 9783540929567

This ebook 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 conscientiously reviewed and chosen from 247 submissions. The symposium positive aspects eight significant issues together with all points of photo and video expertise: photo sensors and multimedia undefined; pictures and visualization; photograph and video research; reputation and retrieval; multi-view imaging and processing; machine imaginative and prescient functions; video communications and networking; and multimedia processing. The papers are prepared in topical sections on faces and pedestrians; panoramic photographs; neighborhood picture research; association and grouping; multiview geometry; detection and monitoring; computational images and forgeries; coding and steganography; acceptance 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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The format of this paper is as follows; Section 2) illustrates our methodology and its advantages; Section 3) explains how an FQR can be learned using an MLP neural network (NN), and proves that the rating can predict the probability of recognition success; Section 4) explains and reports on the experiments that use the MLP NN described in section 3; and finally Section 5) concludes our findings. Using Face Quality Ratings to Improve Real-Time Face Recognition 2 15 Methodology A FQR is obtained by using an MLP NN that has been trained with a specific face recognition method and a training database.

437–442 (2007) Using Face Quality Ratings to Improve Real-Time Face Recognition Karl Axnick, Ray Jarvis, and Kim C. au Abstract. A Face Quality Rating (FQR) is a value derived from a face image that indicates the probability that the face image will be successfully recognized by a specific face recognition method. The FQR can be used as a pre-filter in real-time environments where thousands of face images can be captured every second by multiple surveillance cameras. With so many captured face images, face recognition methods need to strategically decide which face images to attempt recognition on, as it is prohibitively difficult to attempt recognition on all of the images.

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 .

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