By Maria De Marsico, Michele Nappi, Daniel Riccio (auth.), Toshikazu Wada, Fay Huang, Stephen Lin (eds.)
This ebook constitutes the refereed complaints 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 factors eight significant subject matters together with all elements of photo and video know-how: photo sensors and multimedia undefined; photographs and visualization; picture and video research; popularity and retrieval; multi-view imaging and processing; laptop imaginative and prescient functions; video communications and networking; and multimedia processing. The papers are geared up 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; acceptance 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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Additional info for Advances in Image and Video Technology: Third Pacific Rim Symposium, PSIVT 2009, Tokyo, Japan, January 13-16, 2009. Proceedings
These approaches have enjoyed considerable success in conventional face recognition settings, but they all rapidly fail in diﬃcult real-time environments as the number of people in the scene increases (refer to Fig. 5). This failure can be attributed to T. Wada, F. Huang, and S. ): PSIVT 2009, LNCS 5414, pp. 13–24, 2009. c Springer-Verlag Berlin Heidelberg 2009 14 K. Axnick, R. C. Ng the decision in the approaches that if a face is detected, it must have recognition attempted. Although this decision seems logical given the ﬁeld, unless the face recognition approach is 100% accurate the decision is going to waste valuable processor time trying to recognize every single face the system detects.
Our keypoint tracker sometimes loses features when they became occluded or leave an image. 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.
C. Ng that with 4 or 5 cameras running at 25 frames per second and hundreds of people objects constantly entering and exiting the scene at random intervals, that the “to do” list can grow extremely rapidly. Only when a person is recognized using our face recognition methods (SFP and PCA), are their image captures removed from the “to do” list. This environment lets us test many hypotheses. Of note however is the eﬀect of changing the maximum population allowed in the scene and the eﬀect of FQR assistance when the recognition methods’ speeds were varied.