| Koivo, A. J., Kim, C. W. " Robust Image Modeling for Classification of Surface Defects on Wood Boards " IEEE Trans. On SMC Vol 19, no 6 pp 1659 - 1666 |
....[18] While these algorithms are optimal for Gaussian noise, their performance is severely deteriorated by a few outliers, which is common in practical computer vision applications. To cope with this problem, M estimators based on ML were applied to some computer vision problems [19] 20] [21], 22] Also, a robust least median squares (LMedS) algorithm having a high breakdown point was successfully applied to visual reconstruction problems [23] and moton estimation [24] where the breakdown point denotes a performance measure representing the percentage of outliers that an estimator ....
# A.J. Koive and C.W. Kim, "Robust Image Modeling for Classification of Surface Defects on Wood Boards," IEEE Trans. Systems, Man, and Cybernetics, vol. 19, no. 6, pp. 1,659-1,666, Nov. 1989.
No context found.
Koivo, A. J., Kim, C. W. " Robust Image Modeling for Classification of Surface Defects on Wood Boards " IEEE Trans. On SMC Vol 19, no 6 pp 1659 - 1666
No context found.
A.J. Koivo and C.W. Kim. Robust image modeling for classification of surface defects on wood boards. IEEE Transactions on Systems, Man, and Cybernetics, 19(6):1659--1666, 1989.
No context found.
A. J. Koivo and C. W. Kim, "Robust image modeling for classification of surface defects on wood boards," IEEE Trans. on System, Man, and Cybernetics, vol. 19, no. 6, pp. 1659-1666.
No context found.
A. Koivo and C. Kim, "Robust image modeling for classification of surface defects on wood boards," IEEE Trans. SMC-19, pp. 1659-1666, Nov/Dec. 1989.
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