By Maria De Marsico, Michele Nappi, Daniel Riccio (auth.), Toshikazu Wada, Fay Huang, Stephen Lin (eds.)
This booklet constitutes the refereed lawsuits 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 beneficial properties eight significant issues together with all features of photograph and video know-how: photograph sensors and multimedia undefined; photographs and visualization; snapshot and video research; acceptance and retrieval; multi-view imaging and processing; computing device imaginative and prescient purposes; video communications and networking; and multimedia processing. The papers are prepared in topical sections on faces and pedestrians; panoramic photographs; neighborhood photo 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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Additional info for Advances in Image and Video Technology: Third Pacific Rim Symposium, PSIVT 2009, Tokyo, Japan, January 13-16, 2009. Proceedings
We calculated the Euclidean distance between each tracked keypoint and ground-truth. If the distance was below the threshold, the tracked keypoint was determined to be a successfully tracked point. We then computed a tracking success rate from the total number of successfully tracked points. 3 Results Figure 6 shows the tracking success rate calculated from all the frames (180 per frames for each sequence) used in 5 sequences. The horizontal axis represents 32 Y. Tsuduki and H. Fujiyoshi Fig. 6.
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. It is informative for pedestrian classiﬁcation. Mathematically, a co-occurrence matrix C is deﬁned over an n × m image I, parameterized by an oﬀset (x, y), as: n m Cx,y (i, j) = p=1 q=1 1, if I(p, q) = i and I(p + x, q + y) = j 0, otherwise.
Even if face recognition is always attempted regardless of a person’s FQR value, the FQR value can: a) generate a conﬁdence value in the match score, b) cause the required threshold for successful matches to be scaled up, and c) help explain if a target’s input image fails to be recognized because the target is not in the database or because the target’s input image is too poor in face quality to match with the target’s database image. Many face recognition systems rely on face detection  as a quality check.