Download Advances in Visual Computing: Third International Symposium, by Pier-Luc St-Onge, Guillaume-Alexandre Bilodeau (auth.), PDF

By Pier-Luc St-Onge, Guillaume-Alexandre Bilodeau (auth.), George Bebis, Richard Boyle, Bahram Parvin, Darko Koracin, Nikos Paragios, Syeda-Mahmood Tanveer, Tao Ju, Zicheng Liu, Sabine Coquillart, Carolina Cruz-Neira, Torsten Müller, Tom Malzbender (eds.)

It is with nice excitement that we welcome you to the court cases of the third - ternational Symposium on visible Computing (ISVC 2007) held in Lake Tahoe, Nevada/California. ISVC o?ers a standard umbrella for the 4 major parts of visualcomputing together with vision,graphics,visualization,andvirtualreality.Its target is to supply a discussion board for researchers, scientists, engineers and practitioners through the international to offer their newest learn ?ndings, principles, devel- ments, and purposes within the broader quarter of visible computing. Thisyear,theprogramconsistedof14oralsessions,1postersession,6special tracks, and six keynote shows. Following a really winning ISVC 2006, the reaction to the decision for papers used to be virtually both robust; we acquired over 270 submissions for the most symposium from which we accredited seventy seven papers for oral presentation and forty two papers for poster presentation. specific music papers have been solicited individually in the course of the Organizing and software Committees of every music. a complete of 32 papers have been approved for oral presentation and five papers for poster presentation within the precise tracks.

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Additional info for Advances in Visual Computing: Third International Symposium, ISVC 2007, Lake Tahoe, NV, USA, November 26-28, 2007, Proceedings, Part II

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A video sequence V of human motion can be considered to be the variation of a specific human pose as a function of time. Let P = {P1 , . . , PK } represent a discrete set of K poses sampled from the space of all possible poses that are representative of a specific human motion type, where K is a small number, usually much smaller than the number of frames in the video sequence. Let M represent the spatial-temporal information extracted from the video sequence. This information describes temporal variations in the image frames, and can be obtained from measurements such as optical flow and spatial-temporal features.

25 (b). Classification. We compared our results with the results reported by Niebles and Fei Fei [15]. Similarly, we adopted a leave-one-out scheme for evaluation, by taking videos of one subject as testing data, and using sequences of the remaining subjects for training. The segmentation of sequences is not required by our method. Additionally, only the best match for each model is considered when making the labeling decision. For a given motion type we selected one motion cycle from every original training sequences.

75, and t = 1. The aligned sequences serve as the input to the dynamic model learning algorithm. The learning procedure is divided into two main steps. Fig. 2. Examples of segmented and normalized walking sequences, and background images. 75 and t = 1. First, the algorithm estimates the parameters for each of the partial models. Secondly, the parameters representing the spatial-temporal configuration of the global model are determined. The learning steps of our algorithm are detailed next. For simplicity, the probabilities in our model are represented by Gaussian densities.

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