Abstract
In the context of image processing, a major role is played by the features and primitives that describe the data under examination and on which the processing operation is performed. Images acquired by different sensors, for different parameter values tunings, and multi-dimensional and multi-temporal data are becoming easily available, thus increasing the dimensionality of the classification space, then the need for feature-selection techniques.
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References
Chen, C. H., Pau, L. F., and Wang, P. S. P. Handbook of Pattern Recognition and Computer Vision. World Scientific, 1993.
Haralick, R. M., and Shapiro, L. G. Computer Vision and Robot Vision, vol. I and II, Addison-Wesley, 1992.
Fu, K. S., and Mui, J. K. A survey on image segmentation. Pattern Recognition, 13:3–16, 1981.
Shahshahani, B. M., and Landgrebe, D. The Effect of Unlabeled Samples in Reducing the Small Sample Size Problem and Mitigating the Hughes Phenomenon. IEEE Transactions on Geoscience and Remote Sensing, 32(5):1087–1095.
Zhang, Y. J. A Survey on Evaluation Methods for Image Segmentation. Pattern Recognition, 29(8):1335–1346, 1996.
Beardslee, D., and Wertheimer, M., Principles of perceptual organization. Readings in Perception, Van Nostrand, Princeton, NJ, 1958, pp. 115–135.
Dellepiane, S. The active role of 2D and 3D images: semi-automatic segmentation. Contemporary Perspectives in Three-Dimensional Biomedical Imaging, 1997, ch 7, Roux C. and Coatrieux J.L. Eds., IOS Press.
Kass, M., Witkin, A., and Terzopoulos, D. Snakes: active contour models. Proc. First Int. Conf. Comp. Vision (1987), pp. 259–268.
Rignot, E., and Chellappa, R. Segmentation of Polarimetric Synthetic Aperture Radar data. IEEE Transactions on Image Processing, 1(3):281–300, 1992.
Vincent, L., and Soille, P. Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(6):583–598, 1991.
Dellepiane, S., Fontana, F., and Vernazza, G. Nonlinear image labelling for multivalued segmentation. IEEE Transactions on Image Processing, 5(3):429–446, 1996.
Thiran, J. P., and Macq, B. Morphological feature extraction for the classification of digital images of cancerous tissues. IEEE Transactions on Biomedical Engineering, 43(10):1011–1020, 1996.
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Dellepiane, S.G. (2000). The Importance of Features and Primitives for Multi-dimensional/Multi-channel Image Processing. In: Lisboa, P.J.G., Ifeachor, E.C., Szczepaniak, P.S. (eds) Artificial Neural Networks in Biomedicine. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0487-2_20
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DOI: https://doi.org/10.1007/978-1-4471-0487-2_20
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