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I.J. Cox, J.B. Kruskal, D.A. Wallach, "Predicting and Estimating the Accuracy of a Subpixel Registration Algorithm," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 8, pp. 721734, August, 1990.  
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@article{ 10.1109/34.57665, author = {I.J. Cox and J.B. Kruskal and D.A. Wallach}, title = {Predicting and Estimating the Accuracy of a Subpixel Registration Algorithm}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {12}, number = {8}, issn = {01628828}, year = {1990}, pages = {721734}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.57665}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Predicting and Estimating the Accuracy of a Subpixel Registration Algorithm IS  8 SN  01628828 SP721 EP734 EPD  721734 A1  I.J. Cox, A1  J.B. Kruskal, A1  D.A. Wallach, PY  1990 KW  computer vision; accuracy; subpixel registration algorithm; quantized video image; solid triangle; image point data; edge detection; direct measurement; prediction theory; computer vision; estimation theory; iterative methods VL  12 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
It is shown that an efficient practical registration algorithm previously described by H.G. Barrow et al. (1977) can provide highaccuracy registration. Experiments with a quantized video image of a solid triangle yielded registration that was accurate to 3% of the interpixel spacing (i.e. accurate to 0.5 mils) in the x and y directions and 0.015 degrees in rotation. It may also be important to predict the accuracy in advance, to see whether specifications can be met and to estimate accuracy during registration, in order to control quality. The authors provide practical formulas for both purposes for two kinds of image point data: edge detection (ED) data and direct measurement (DM) data. In two experiments using ED data, the predicted, estimated, and observed accuracies are all in agreement. The prediction theory developed suggests five precautions to avoid loss of registration accuracy. Perhaps most important is Precaution c, the necessity of the ED case not to have a large fraction of the total segment length of the model aligned with the horizontal or vertical directions of the pixel grid. When the model consists largely of horizontal and vertical segments, a good way to observe this precaution is to tilt the pixel grid a few degrees away from perfect alignment, e.g. by tilting the video camera. A third experiment verifies that violating Precaution c can seriously degrade accuracy.
[1] H. G. Barrow, J. M. Tenenbaum, R. C. Bolles, and H. C. Wolf, "Parametric correspondence and chamfer matching: Two new techniques for image matching," inProc. Int. Joint Conf. Artificial Intelligence, 1977, pp. 659663.
[2] C. A. Berenstein, L. N. Kanal, D. Lavine, and E. Olson, "A geometric approach to subpixel registration accuracy,"Comput. Vision Graphics Image Processing, vol. 40, no. 3, pp. 334360, 1987.
[3] R. A. Boie and I. J. Cox, "On optimum edge recognition using matched filters," inProc. IEEE Conf. Computer Vision and Pattern Recognition, IEEE, 1986, pp. 100108.
[4] R. A. Boie and I. J. Cox, "Two dimensional optimum edge recognition using matched and Wiener filters for machine vision," inProc. IEEE First Int. Conf. Computer Vision, IEEE, 1987, pp. 450456.
[5] G. Borgefors, "Hierarchical chamfer matching: A parametric edge matching algorithm,"IEEE Trans. Pattern Anal. Machine Intell., vol. 10, no. 6, pp. 849865, Nov. 1988.
[6] I. J. Cox, "Blanche: Position estimation for an autonomous robot vehicle," inProc. IEEE/RSJ Int. Workshop Intelligent Robots and Systems, IEEE, Sept. 1989.
[7] I. J. Cox and J. B. Kruskal, "On the congruence of noisy images to line segment models," inProc. Second Int. Conf. Computer Vision, Tampa, FL, 1988, pp. 252258.
[8] I. J. Cox and J. B. Kruskal, "Determining the 2 or 3dimensional similarity transformation between a point set and a model made of lines and arcs," inProc. 28th IEEE Conf. Decision and Control, IEEE, Dec. 1989.
[9] W.E.L. Grimson, "The combinatorics of object recognition in cluttered environments using constrained search," inProc. 1988 Int. Conf. Computer Vision (ICCV '88), 1988, pp. 218227.
[10] F. P. Preparata and M. I. Shamos,Computational Geometry, an Introduction. New York: SpringerVerlag, 1985.
[11] G. Stockman, "Object recognition and localization via pose clustering,"Comp. Vision Graphics Image Processing, vol. 40, pp. 361387, 1987.
[12] E. J. Williams, "Linear hypothesis: Regression," inInternational Encyclopedia of Statistics, vol. 1, W. H. Kruskal and J. M. Tanur, Eds: New York: The Free Press (Macmillan), 1978, pp. 523541.