Z. Sun, V. Ramesh, and A. M. Tekalp, "Error characterization of the factorization method," Comput. Vis. Image Understanding, vol. 82, pp. 110--137, May 2001.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....has been the central problem in computer vision for the past two decades. Extensive literature on the subject can be found in [1] 2] and [3] among others. Recent research on SfM issues has concentrated on sensitivity, robustness and error characterization of existing techniques [4] 5] 6] [7], etc. The errors that affect the quality of SfM algorithms can be broadly classified into two groups geometrical and statistical. The geometrical errors arise because of the well known ambiguities (e.g. the scale ambiguity) present in the mathematical description of the problem (see [8] or ....

....algorithms. Oliensis emphasized the need to understand algorithm behavior and the characteristics of the natural phenomenon that is being modeled [3] Ma, Kosecka and Sastry [6] also addressed the issues of sensitivity and robustness in their motion recovery algorithm. Sun, Ramesh and Tekalp [7] proposed an error characterization of the factorization method for 3D shape and motion recovery from image sequences using matrix perturbation theory. Morris, Kanatani and Kanade [33] analyzed the non trivial effects of unknown scale factor, referred to in the literature as gauge freedom, on the ....

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Z. Sun, V. Ramesh, and A.M. Tekalp, "Error characterization of the factorization method," Computer Vision and Image Understanding, vol. 82, pp. 110--137, May 2001.

....algorithms. Oliensis emphasized the need to understand algorithm behavior and the characteristics of the natural phenomenon that is being modeled [16] Ma, Kosecka and Sastry [13] also addressed the issues of sensitivity and robustness in their motion recovery algorithm. Sun, Ramesh and Tekalp [22] have proposed an error characterization of the factorization method for 3 D shape and motion recovery from image sequences using matrix perturbation theory. Morris and Kanatani have extended the covariance based uncertainty calculations to account for the geometric indeterminacies like scale ....

....A ip and A iq are now functions of the inverse depth estimates h i . The covariance of the feature points, R u , is obtained using the standard method for estimating the error covariance using the inverse of the Hessian matrix of the second partial derivatives of the intensity along x and y axes [22]. Large systematic errors in feature correspondences are not considered in the above analysis. Such errors need to be addressed using robust estimation techniques. A detailed analysis of robust estimation of structure and motion using least median of squares can be found in [18] 3.2 ....

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Z. Sun, V. Ramesh, and A.M. Tekalp. Error characterization of the factorization method. Computer Vision and Image Understanding, 82:110--137, May 2001.

....much to be desired. This has led many researchers to analyze the sensitivity, robustness and statistical error characterization of the existing algorithms, trying to understand algorithm behavior and the characteristics of the natural phenomenon that is being modeled [3] 4] 5] 6] 7] 8] [9]. To overcome these errors, the tendency has been to add redundancy in the information processed. This raises the question as to how the redundant information affects the quality of the final solution. In this paper, we consider the situation where multiple reconstructions of the same scene are ....

Z. Sun, V. Ramesh, and A.M. Tekalp, "Error characterization of the factorization method," Computer Vision and Image Understanding, vol. 82, pp. 110--137, May 2001.

....algorithms. liensis emphasized the need to understand algorithm behavior and the characteristics of the natural phenomenon that is being modeled [3] Ma, Kosecka and Sastry [19] also addressed the issues of sensitivity and robustness in their motion recovery algorithm. Sun, Ramesh and Tekalp [20] proposed an error characterization of the factorization method for 3D shape and motion recovery from image sequences using matrix perturbation theory. Morris, Kanatani and Kanade [21] analyzed the non trivial effects of unknown scale factor, referred to in the literature as gauge freedom, on the ....

....the optical flow was estimated a priori over the first few frames of the video sequence, which were not used in the reconstruction. It was done over a sampled grid of points (rather than the dense flow) so as to simplify calculations. The technique used is similar to the gradient based method of [20], except that, for more accurate results, it was repeated for each of these initial frames and the final estimate was obtained using bootstrapping techniques [38] This is the stage where the quality of the video data is estimated and incorporated into the algorithm. Assuming that the statistics ....

Z. Sun, V. Ramesh, A. Tekalp, Error characterization of the factorization method, Computer Vision and Image Understanding 82 (2001) 110-137.

....algorithms. Oliensis emphasized the need to understand algorithm behavior and the characteristics of the natural phenomenon that is being modeled [7] Ma, Kosecka and Sastry [15] also addressed the issues of sensitivity and robustness in their motion recovery algorithm. Sun, Ramesh and Tekalp [16] proposed an error characterization of the factorization method for 3D shape and motion recovery from image sequences using matrix perturbation theory. Morris, Kanatani and Kanade [17] analyzed the non trivial e ects of unknown scale factor, referred to in the literature as gauge freedom, on the ....

Z. Sun, V. Ramesh, and A.M. Tekalp, \Error characterization of the factorization method," Computer Vision and Image Understanding, vol. 82, pp. 110-137, May 2001.

....value decomposition of a given measurement matrix subject to rank 3 constraint. It was later extended to more general camera models, such as weak perspective [2] para perspective [3] affine [4] and projective factorization [5] It was also extended to in clude uncertainty handling [6] [8]. Sequential or recursive methods [9] 10] 11] compute and filter 3 D shape and motion estimates by Kalman filtering such that the reprojections 2 have the minimum distance to the (2D tracking) observations. Bundle adjustment method [12] 13] 14] usually involves a nonlinear optimization ....

Zhaohui Sun, V. Ramesh, and A. Murat Tekalp, "Error characterization of the factorization method," Computer Vision and Image Understanding, vol. 82, pp. 110-137, May 2001.

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Z. Sun, V. Ramesh, and A. M. Tekalp, "Error characterization of the factorization method," Comput. Vis. Image Understanding, vol. 82, pp. 110--137, May 2001.

No context found.

Z. Sun, V. Ramesh, and A.M. Tekalp, "Error characterization of the factorization method," Computer Vision and Image Understanding, vol. 82, pp. 110--137, May 2001.

No context found.

Z. Sun, V. Ramesh, and A.M. Tekalp. Error characterization of the factorization method. Computer Vision and Image Understanding, 82:110--137, May 2001.

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Z. Sun, V. Ramesh, and A.M. Tekalp, "Error characterization of the factorization method," Computer Vision and Image Understanding, vol. 82, pp. 110--137, May 2001.

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