Computer Vision – ACCV 2006: 7th Asian Conference on by Edmond Boyer (auth.), P. J. Narayanan, Shree K. Nayar,

By Edmond Boyer (auth.), P. J. Narayanan, Shree K. Nayar, Heung-Yeung Shum (eds.)

Welcometothe7thAsianConferenceonComputerVision.Itgivesusgreatpl- certain to bring about its court cases. ACCV has been making its rounds in the course of the Asian panorama and got here to India this 12 months. we're pleased with the technical software we have now prepare and we are hoping you take pleasure in it. curiosity in laptop imaginative and prescient is expanding and ACCV 2006 attracted approximately 500 submission. The assessment group consisted of 27 specialists serving as region Chairs and approximately 270 reviewers in all. the entire method used to be performed electronically in a double-blind manner,a ?rstfor ACCV. Eachpaper wasassignedto a space Chair who chanced on 3 useful reviewers for it. We have been in a position to include the utmost load at the reviewers to 9 and the typical load to under six. The evaluate shape had house for qualitative and quantitative evaluate of the paper on 9 facets. The submitted studies underwent an tricky method. First, they have been visible through the realm Chair, who resolved divergences of opinion between reviewers, if any. the realm Chair then wrote qualitative reviews and a quantitative ranking in addition to his/her preliminary advice at the paper. those have been checked out by way of application Co-chairs and compiled right into a probables checklist. the world Chairs and application Co-chairs met in Beijing in the course of ICCV to debate this record and arrivedat the ?nal record of sixty four oral papers and 128 posters. obviously, many deserving papers couldn't be accommodated.

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Additional info for Computer Vision – ACCV 2006: 7th Asian Conference on Computer Vision, Hyderabad, India, January 13-16, 2006. Proceedings, Part I

Example text

The distance between On Using Silhouettes for Camera Calibration 7 angle translation focal length Fig. 5. 4π] with α the true value. The filled points denote the limit distances on curves above which the 2 cones do not intersect at all. 2 viewing cones is then simply defined by a double integration over the 2 concerned cones. Recall that silhouettes and viewing cones are discrete in practice and thus defined by sets of contour points in the images and boundary rays in space. The simplest solution consists then in summing individual distances over boundary rays.

Figure 6 shows, on the left, the silhouette calibration ratios after optimization; and on the right, relative errors in the estimated camera parameters after optimization using 5 frames per cameras. These results first validate the silhouette calibration ratio as a global estimator for the quality of any calibration with respect to silhouette data. Second, they show that using only one frame per camera is intractable in most situations. However, they prove also that using several frames, calibration can be recovered with a good precision even far from the exact solution.

3. The cost function we minimize is the sum of the two squared residuals of the equations (4) in which we use the simplified form of the ICP (11): N f (α, ρ, φ) = (xT1 HjT ωHj x1 − xT2 HjT ωHj x2 )2 + (xT1 HjT ωHj x2 )2 . j=2 We derived the symbolic expression of the residuals. At each function evaluation, the developped residuals are numerically evaluated. This choice seems to be a good compromise between the evaluation time and the quality of the function extension. We have implemented a C++ code based on the PROFIL/BIAS library2 .

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