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Part of the book series: Computational Imaging and Vision ((CIVI,volume 17))

Abstract

Performance evaluation is a difficult and very challenging task. In spite of many discussions in the literature, e.g., (Haralick et al., 1994), and well understood goals, e.g., (Christensen and Förstner, 1997; Haralick, 1994), there is a wide gap between what performance assessment using simple, synthetic data predicts and what is obtained when the same algorithms are applied to real data.

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References

  • Cho, K. and Meer, P. (1997) Image segmentation from consensus information, Computer Vision and Image Understanding, 68: 72–89.

    Article  Google Scholar 

  • Cho, K. and Meer, P. and Cabrera, J. (1997) Performance assessment through bootstrap, IEEE PAMI, 19: 1185–1198.

    Article  Google Scholar 

  • Davidson, A.C. and Hinkley, D.V. (1997) Bootstrap Methods and their Application, Cambridge University Press, Cambridge, UK.

    Google Scholar 

  • Eggert, D.W., Lorusso, A. and Fisher, R.B. (1997) Estimating 3-D rigid body transformations: A comparison of four major algorithms., Machine Vision and Applications, 9: 272–290.

    Article  Google Scholar 

  • Christensen, H.I. and Förstner, W. (1997) Performance characteristics of vision algorithms, Machine Vision and Applications, 9: 215–218.

    Article  Google Scholar 

  • Efron, B. and Tibshirani, R.J. (1993) An Introduction to the Bootstrap, Chapman & Hall, London.

    MATH  Google Scholar 

  • Haralick, R.M. (1989) Performance assessment of near-perfect machines, Machine Vision and Applications, 2: 1–16.

    Article  Google Scholar 

  • Haralick, R.M. (1994) Performance characterization protocol in computer vision, 1994 ARPA Image Understanding Workshop, Monterey, CA, 667–673.

    Google Scholar 

  • Haralick, R.M.; Cinque, L., Guerra, C. and Levialdi, S.; Weng, J. and Huang, T.S.; Meer, P.; Shirai, Y.; Draper, B.A. and Beveridge, J.R. (1994) Dialogue: Performance characterization in computer vision, CVGIP: Image Understanding, 60: 245–265.

    Article  Google Scholar 

  • Heath, M.D., Sarkar, S., Sanocki, T. and Bowyer, K.W. (1997) A robust visual method for assessing the relative performance of edge-detection algorithms, IEEE PAMI, 19: 13381359.

    Google Scholar 

  • Hoover, A., Gillian, J.-B., Jiang, X., Flynn, P.J., Bunke, H., Goldgof, D.B., Bowyer, K.W., Eggert, D.W., Fitzgibbon, A. and Fisher, R.B. (1996) An experimental comparison of range image segmentation algorithms, IEEE PA MI, 18: 673–689.

    Article  Google Scholar 

  • Matei, B., Meer, P. and Tyler, D. (1998) Performance assessment by resampling: Rigid motion estimators, Empirical Evaluation Techniques in Computer Vision, Bowyer, K.W., Phillips, P.J. (eds.), IEEE CS Press, Los Alamitos, CA, 72–95.

    Google Scholar 

  • Ramesh, V. and Haralick, R.M. (1994) An integrated gradient edge detector–Theory and performance evaluation, 1994 ARPA Image Understanding Workshop, Monterey, CA, 689–702.

    Google Scholar 

  • Umeyama, S. (1991) Least-squares estimation of transformation parameters between two point patterns, IEEE PAMI, 13: 376–380.

    Article  Google Scholar 

  • Wang, Z. and Jepson, A. (1994) A new closed-form solution for absolute orientation, IEEE Conference on Computer Vision and Pattern Recognition 1994, Seattle, WA, 129–134.

    Chapter  Google Scholar 

  • Yi, S., Haralick, R.M. and Shapiro, L.G. (1994) Error propagation in machine vision, Machine Vision and Applications, 7: 93–114.

    Article  Google Scholar 

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© 2000 Springer Science+Business Media Dordrecht

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Meer, P., Matei, B., Cho, K. (2000). Input Guided Performance Evaluation. In: Klette, R., Stiehl, H.S., Viergever, M.A., Vincken, K.L. (eds) Performance Characterization in Computer Vision. Computational Imaging and Vision, vol 17. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9538-4_10

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  • DOI: https://doi.org/10.1007/978-94-015-9538-4_10

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-5487-6

  • Online ISBN: 978-94-015-9538-4

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