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

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 lawsuits 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, pics, visualization, and digital fact. The aim is to supply a discussion board for researchers, scientists, engineers, and pr- titioners through the global to provide their newest study ?ndings, rules, advancements, and purposes within the broader region of visible computing. This yr, this system consisted of 14 oral classes, one poster consultation, 7 designated tracks, and six keynote shows. The reaction to the decision for papers was once first-class; we bought 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. unique tune papers have been solicited individually during the Organizing and application Committees of every song. a complete of forty four papers have been permitted for oral presentation and six papers for poster presentation within the certain tracks.

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

Sample text

K-means is used for clustering followed by the EM algorithm for learning a lexicon of image words. Though some promising results are reported, it is mentioned that the approaches have certain limitations and does not work well on some objects. Locally normalized histogram of Lunar Image Classification for Terrain Detection (a) (b) (c) 3 (d) Histograms of Gradient Orientation Luminance Histograms Fig. 2. Examples of lunar terrain types and the corresponding feature examples at the bottom: (a) crater with shadow (b) crater without shadow (c) shadow area (d) flat area gradient orientations is adopted in [2] for person detection.

We note that the reconstructed curves maintain the main features of the original data. In our case we wanted the Bezier curves to capture the general shape, perimeter or circumference and volume of the original cc contours. 20 coefficients 40 coefficients 60 coefficients 80 coefficients 100 coefficients 120 coefficients 140 coefficients 160 coefficients Fig. 2. Bezier curve reconstruction of the corpus callosum using different number of coefficients, the original curve is shown in red while the reconstructed one is shown in green Fig.

T. Cheng et al. 6 Conclusions and Future Work In this paper, we have investigated image feature and classifiers for lunar terrain classification. 56%. We have also shown that the proposed approach outperforms Haar-like features and luminance histogram by around 30%. The system, along with the open-source Lunar Image Labeling Toolkit, can be applied to larger orbital image mining package for imagery from NASA space missions such as LCROSS and HiRISE. For future work, we plan to focus on automatic terrain patch detection using scaleinvariant image descriptors.

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