Burt, P. J., Yen, C., Xu, X. Local Correlation Measures for Motion Analysis A Comparative Study, Image Processing Laboratory Technical Report, IPL-TR-024, 1982  Home/Search   Document Not in Database   Summary   Related Articles   Check

....exist. Many metrics of texture have been proposed, but intuitively, measures of local intensity variation, edginess, or gradient magnitude will suffice for a qualitative assessment. Good cross correlation implies a high degree of similarity between the template and the candidate mosaic region [5]. It is important to recognize that this second condition does not imply the first because even relatively textureless regions may correlate perfectly. Of course, any template autocorrelation surface peak always has a value of unity, so it is the normalized cross correlation Figure 4: ....

P. J. Burt, C. Yen, and X. Xu, "Local correlation measures for motion analysis: a comparative study." IEEE CPRIP, 269-274, 1982.

....of the National Conference on Artificial Intelligence, AAAI 90 , Boston, Mass, 1990 Constraints for the Early Detection of Discontinuity from Motion Michael J. Black and P. Anandan Department of Computer Science Yale University New Haven, CT 06520 2158 Abstract Surface discontinuities are detected in a sequence of images by exploiting physical constraints at early stages in the processing of visual motion. To achieve accurate early discontinuity detection we exploit five physical constraints on the presence of discontinuities: ....

....of the National Conference on Artificial Intelligence, AAAI 90 , Boston, Mass, 1990 Constraints for the Early Detection of Discontinuity from Motion Michael J. Black and P. Anandan Department of Computer Science Yale University New Haven, CT 06520 2158 Abstract Surface discontinuities are detected in a sequence of images by exploiting physical constraints at early stages in the processing of visual motion. To achieve accurate early discontinuity detection we exploit five physical constraints on the presence of discontinuities: the ....

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P. J. Butt, C. Yen, and X. Xu. Local correlation measures for motion analysis a comparative study. Technical Report IPL-TR-024, Image Processing Lab., Rensselaer Polytechnic Institute, 1982.

....flow estimation has proceeded along the line suggested by Horn and Schunck [27] displacement estimation using correlation based methods or sums of squared differences has proven to be effective. Comparisons of typical correlation based marchers can be found in Hannah [17] and Burr, Yen and Xu [9]. An in depth study of the applications of these techniques to the estimation of displacement fields in motion sequences is presented by Ariandan [2] In particular, correlation based estimation has proven to be useful when large interframe displacements occur. On the other hand, the method ....

P. J. Burr, C. Yen, and X. Xu. Local correlation measures for motion analysis: a comparative study. In Proceedings of the IEEE Conference on Pattern Recognition and Image Processing, 1982.

....are fewer comparisons between approaches. Reference [19] derives an optimal feature tracking scheme within the gradient search framework, but the limitations of this framework are not addressed. An empirical study of five template matching algorithms in the presence of various image distortions [4] found that NCC provides the best performance in all image categories, although one of the cheaper algorithms performs nearly as well for some types of distortion. A general hierarchical framework for motion tracking is discussed in [1] A correlation based matching approach is selected though ....

P. J. Burt, C. Yen, X. Xu, "Local Correlation Measures for Motion Analysis: a Comparitive Study", IEEE Conf. Pattern Recognition Image Processing 1982, pp. 269-274.

....of the template. Papanikolopoulos [19] uses the SSD measure to generate tracking results that are then used for robotic visual servoing experiments. Anandan [1] and Singh and Allen [23] use this SSD metric for the computation of image ow. Alternative dissimilarity measures can be found in [2] [4] [8] and [20] 2.3 Windowing Often, the SSD measure is not computed for the entire input image, but only for some search window in the input image. Primarily for computational reasons, this restriction also serves as a focus of attention for the feature tracking algorithm. Singh and Allen [22] ....

P. J. Burt, C. Yen, and X. Xu. Local correlation measures for motion analysis: A comparative study. In Proceedings IEEE Conference Pattern Recognition Image Processing, pages 269-274, 1982.

....2.5.3.2 Similarity metrics A similarity metric is used to compare the feature template described above to some area of an image. Section 2.5.3.3 describes the use of these similarity metrics for tracking. In this section, we describe some similarity metrics from the literature. Burt et al. [43] give a good review and compare the computational cost of five local correlation measures in one dimension, concluding that the computationally simpler measures such as direct correlation perform nearly as well as the more complex measures on band pass filtered images. Definitions for these ....

P. J. Burt, C. Yen, and X. Xu, "Local correlation measures for motion analysis: A comparative study," in Proceedings IEEE Conference Pattern Recognition Image Processing, 1982, pp. 269--274.

....several simulations and experiments. 1 Introduction IEEE Conference on Computer Vision and Pattern Recognition (CVPR94) Seattle, June 1994 Is feature tracking a solved problem The extensive studies of image correlation [4] 3] 15] 18] 7] 17] and sum of squared difference (SSD) methods [2], 1] show that all the basics are in place. With small inter frame displacements, a window can be tracked by optimizing some matching criterion with respect to translation [10] 1] and linear image deformation [6] 8] 11] possibly with adaptive window size[14] Feature windows can be ....

P. J. Burt, C. Yen, and X. Xu. Local correlation measures for motion analysis: a comparative study. IEEE CPRIP, 269--274, 1982.

....correlation E(u; v) X k;l I 1 (x u k; y v l)I 0 (x k; y l) 1) or by minimizing the sum of squared differences (SSD) E(u; v) X k;l [I 1 (x u k; y v l) Gamma I 0 (x k; y l) 2 (2) These approaches have been extensively studied and used. See [Ryan et al. 1980; Burt et al. 1982; Horn, 1983; Opitz, 1983; Wood, 1983] for some comparative analyses, and [Forstner, 1987] for a review of statistical aspects of photogrammetry. To obtain sub pixel registration accuracies, a number of possible extension to the basic search technique can be used [Tian and Huhns, 1986] ....

P. J. Burt, C. Yen, and X. Xu. Local correlation measures for motion analysis: a comparative study. In IEEE Conference on Pattern Recognition and Image Processing (PRIP'82), pages 269--274, IEEE Computer Society Press, 1982.

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Burt, P. J., Yen, C., Xu, X. Local Correlation Measures for Motion Analysis A Comparative Study, Image Processing Laboratory Technical Report, IPL-TR-024, 1982

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P. J. Burtand, C. Yen, and X. Xu. Local Correlation Measures for Motion Analysis: a Comparitive Study. In proceedings of the IEEE Conf. Pattern Recognition Image Processing, pages 269--274, 1982.

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P. J. Burt, C. Yen, X. Xu, Local correlation measures for motion analysis: A comparative study, in: Proceedings IEEE Conference Pattern Recognition Image Processing, 1982, pp. 269--274.

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Burt P.J., Yen C., Xu X., "Local Correlation Measures for Motion Analysis: A comparative study," Proc. IEEE Conf. Pattern Recognition Image Processing, pp. 269-274, 1982.

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