Abstract
Robustness of computer vision algorithms requires stability of the computed results against variations in the input data caused by noise or modeling uncertainty. In unsupervised image processing tasks like texture segmentation the extracted image partition should provide reliable model estimates of the different texture types. These texture models represent typical properties of textures and they should not depend on the specific texture data available to the algorithm. Instead, the performance of the algorithms should be invariant to within-class texture fluctuations and sample fluctuations which are omnipresent in noisy images. Segmentation solutions have to generalize from the given texture samples to new instances of the same texture types.
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Buhmann, J.M., Puzicha, J. (2000). Unsupervised Learning for Robust Texture Segmentation. 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_16
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DOI: https://doi.org/10.1007/978-94-015-9538-4_16
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