Advances in Visual Computing: 6th International Symposium, by Heng-Tze Cheng, Feng-Tso Sun, Senaka Buthpitiya, Ying Zhang,

By Heng-Tze Cheng, Feng-Tso Sun, Senaka Buthpitiya, Ying Zhang, Ara V. Nefian (auth.), George Bebis, Richard Boyle, Bahram Parvin, Darko Koracin, Ronald Chung, Riad Hammound, Muhammad Hussain, Tan Kar-Han, Roger Crawfis, Daniel Thalmann, David Kao, Lisa Avil

It is with nice excitement that we current the complaints of the sixth Inter- tional, Symposium on visible Computing (ISVC 2010), which was once held in Las Vegas, Nevada. ISVC presents a standard umbrella for the 4 major parts of visible computing together with imaginative and prescient, photos, visualization, and digital truth. The objective is to supply a discussion board for researchers, scientists, engineers, and pr- titioners during the global to give their most up-to-date learn ?ndings, rules, advancements, and functions within the broader sector of visible computing. This yr, this system consisted of 14 oral classes, one poster consultation, 7 targeted tracks, and six keynote displays. The reaction to the decision for papers used to be first-class; we got over three hundred submissions for the most symposium from which we authorised ninety three papers for oral presentation and seventy three papers for poster p- sentation. distinctive song papers have been solicited individually in the course of the Organizing and software Committees of every tune. a complete of forty four papers have been authorized for oral presentation and six papers for poster presentation within the designated tracks.

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Extra info for Advances in Visual Computing: 6th International Symposium, ISVC 2010, Las Vegas, NV, USA, November 29 - December 1, 2010, Proceedings, Part III

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2 Image Feature Extraction Histogram of Gradient Orientation. Histogram of gradient orientation has been used in the field of human detection [2]. -T. Cheng et al. Fig. 3. The eight Haar-like masks used for feature extraction luminance contrast. In contrast with magnitude, the orientation of gradient is of lower granularity and thus less susceptible to high-frequency noises. The computation of gradient orientation can be formulated as: v* = argmax L(n( pi,v)) − L( pi ) v (1) where v ∈ {(i, j) − 1 ≤ i ≤ 1, − 1 ≤ j ≤ 1, i, j ∈ Z} is the orientation of the gradient, which can be one of the following vectors: (1,0), (1,1), (0,1), (-1,1), (-1,0), (-1,-1), (0,1), (1,-1).

After computing the orientation of gradient at each pixel, we sum up the occurrence of each orientation and form a statistical histogram. Since there are eight possible directions, the resulting histogram is an 8-dimensional vector as shown in Figure 2. The intuition behind the use of this feature is inspired by the characteristics of lunar terrains. While craters are generally of circular shape, flat areas have no specific shape or edges, and shadow areas can have obvious edges from darkness to the area with sunlight.

In our earlier work [3], we dealt with the 2D part of the shape modeling problem where only one slice is given per subject, as extension to this work we present a mathematical formulation of 2D and 3D CC shape modeling using Bezier theory. This study can be further extended to study the features derived from these shape models which lend benefit for discrimination between populations such as normal and autistic populations from a clinical MRI study. The paper will deal very briefly with classification of normal and autistic brains using the features extracted from the Bezier curves and surfaces.

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