Advances in Visual Computing: 6th International Symposium, by Michael Hödlmoser, Martin Kampel (auth.), George Bebis, PDF

By Michael Hödlmoser, Martin Kampel (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 Avila (eds.)

ISBN-10: 3642172733

ISBN-13: 9783642172731

ISBN-10: 3642172741

ISBN-13: 9783642172748

It is with nice excitement that we current the court cases of the sixth Inter- tional, Symposium on visible Computing (ISVC 2010), which was once held in Las Vegas, Nevada. ISVC offers a typical umbrella for the 4 major components of visible computing together with imaginative and prescient, portraits, visualization, and digital truth. The aim is to supply a discussion board for researchers, scientists, engineers, and pr- titioners in the course of the international to provide their newest examine ?ndings, principles, advancements, and purposes within the broader region of visible computing. This 12 months, this system consisted of 14 oral periods, one poster consultation, 7 distinct tracks, and six keynote shows. The reaction to the decision for papers was once first-class; we acquired over three hundred submissions for the most symposium from which we accredited ninety three papers for oral presentation and seventy three papers for poster p- sentation. targeted music papers have been solicited individually in the course of the Organizing and software Committees of every song. a complete of forty four papers have been permitted for oral presentation and six papers for poster presentation within the detailed 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 II

Example text

2m } is the set of homography scaling factors of all transferred points. The cost function is written as follows: l=m Fc (H, w, t, λ, β, γ) = P x1l − G1l (t1l ) 2 λ2l Hp x2l − G1l (t2l ) 2 p=1 l=1 l=1 + γγ2l 1 − m +β 2 λ2l Hp3 x2l . e. β → 0), the influence of the second term on curve fitting will be small so that the curve fitting is faithful to the original measured points of the first view; when β → 1, the cost function has equal weights with regard to the points measured on the first view and those transferred from the second view.

Distortion center is fixed at image center (x0 , y0 ) = (320, 240). (a) Noise in the undistorted image relative to the original image, measured by the ratio of the RMSE using estimated parameters to the RMSE using true parameters. (b) Error in estimating λ. Each point is an average over the same 10 runs shown in part (a). Each point is an average over 10 runs. Error bars denote 95% confidence intervals. case, we estimated all three parameters of the distortion model. We compare four methods for arc estimation.

Radiometry, 221–244 (1992) 9. : A simple new method for precise lens distortion correction of low cost camera systems. In: German Pattern Recognition Symposium, pp. 570– 577 (2004) 10. : Fundamental matrix for cameras with radial distortion. In: International Conference on Computer Vision (ICCV), pp. 625–632 (2005) 20 F. N. Dailey 11. : Parameter-free radial distortion correction with center of distortion estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 1309–1321 (2007) 12.

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Advances in Visual Computing: 6th International Symposium, ISVC 2010, Las Vegas, NV, USA, November 29 – December 1, 2010, Proceedings, Part II by Michael Hödlmoser, Martin Kampel (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 Avila (eds.)


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