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 yr. we're happy with the technical application we now have prepare and we are hoping you get pleasure from it. curiosity in machine imaginative and prescient is expanding and ACCV 2006 attracted approximately 500 submission. The overview crew consisted of 27 specialists serving as quarter Chairs and approximately 270 reviewers in all. the full procedure used to be carried out electronically in a double-blind manner,a ?rstfor ACCV. Eachpaper wasassignedto a space Chair who chanced on 3 powerfuble reviewers for it. We have been capable of include the utmost load at the reviewers to 9 and the typical load to under six. The overview shape had house for qualitative and quantitative evaluate of the paper on 9 elements. The submitted reports underwent an complex strategy. First, they have been obvious by means of the world Chair, who resolved divergences of opinion between reviewers, if any. the realm Chair then wrote qualitative reviews and a quantitative ranking besides his/her preliminary suggestion at the paper. those have been checked out via 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 checklist and arrivedat the ?nal record of sixty four oral papers and 128 posters. evidently, many deserving papers couldn't be accommodated.

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

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On PAMI 26 (2004) 1633–1638 12. V. In: Matrix computations. Third ed. John Hopkins University Press (1996) 13. E. In: Interval Analysis. Prentice-Hall (1966) 14. B. In: Rigorous Global Search: Continous Problems. Kluwer Academic Publishers (1996) 15. : Accuracy Bounds and Optimal Computation of Homography for Image Mosaicing Applications. In: Proc. of the ICCV. , Kerkyra, Greece (1999) 73–78 16. : On plane-based camera calibration: A general algorithm, singularities, applications. In: Proc. of the CVPR.

N. M(1:r,1:c) denotes the r × c submatrix of M selected by the row range 1 : r and the column range 1 : c. The notation M(:,1:c), resp. M(1:r,:) , selects the first c (resp. r) columns, resp. rows, of M. We also define the canonical vectors: e1 ≡ (1, 0, 0) , e2 ≡ (0, 1, 0) , e3 ≡ (0, 0, 1) . (1) The matrix [x]× refers to the skew-symmetric, order-3, matrix, such that [x]× y = x × y, y ∈ R3 . In this paper, we will make a heavily use of the equality [Tx]× = √ det(T)T− [x]× T−1 . The notation i always refers to the imaginary number −1.

We thus left-multiply a L(rd , k) to the left-hand side of the equation, and rearrange the result in a bilinear form using Kronecker product symbol ⊗, then get: (3) ((xd , yd , L(rd , k)) ⊗ xc )vec(FT ) = 0. Now we do so for a group of nine points, whose coordinates denoted by matrices X and X. We then stack the resulting nine bilinear equations together, and get a homogeneous equation system: (4) M(X , X, k)f = 0, where the square matrix M is called measurement matrix, which depends explicitly on input distorted coordinates and the distortion parameter k, and f the right null-vector.

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