M. Bober, N. Geogis and J. Kittler, On accurate and robust estimation of fundamental matrix, Comput. Vision Image Understanding , 72-1 (1998), 39--53.  Home/Search   Document Details and Download   Summary   Related Articles

....to observe the performance of our algorithm and illustrate the reliability evaluation process. 1. Introduction Computing the fundamental matrix from corresponding points over two images is one of the basic elements of computer vision. Already, many algorithms have been presented for this purpose [1, 3, 6, 7, 13, 18, 22, 23, 24, 25]. These are roughly classified into two approaches: the bundleadjustment and the linear algorithm. The bundle adjustment is based on maximal likelihood estimation after reprojection: we assume all the parameter values, predict where the image data should ideally be observed, and determine the ....

....is available about the noise behavior. 4. Theoretical Accuracy Bound Let F be an estimate of the fundamental matrix, and F its true value. The uncertainty of the estimate F is measured by its covariance tensor V[ F ] E[P i ( F 0 F ) Omega ( F 0 F ) j P ] 5) where E[ 1 ] denotes expectation. The operator Omega denotes tensor product: for matrices A = A ij ) and B = B ij ) the (ijkl) element of their tensor product is A ij B kl . For tensors P = P ijkl ) and T = T ijkl ) the product PT P is a tensor whose (ijkl) element is P 3 m;n;p;q=1 P ijmn P klpq ....

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M. Bober, N. Geogis and J. Kittler, On accurate and robust estimation of fundamental matrix, Comput. Vision Image Understanding , 72-1 (1998), 39--53.

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