By Sankar K. Pal, Ashish Ghosh, Malay K. Kundu (auth.), Prof. Sankar K. Pal, Dr. Ashish Ghosh, Prof. Malay K. Kundu (eds.)
Any job that comprises decision-making can make the most of gentle computing options which enable untimely judgements to be deferred. The processing and research of pictures isn't any exception to this rule. within the classical picture research paradigm, step one is almost constantly a few kind of segmentation method within which the picture is split into (hopefully, significant) elements. It used to be mentioned approximately 30 years in the past through Prewitt (1] that the choices serious about snapshot segmentation should be postponed by way of concerning the snapshot components as fuzzy, instead of crisp, subsets of the picture. It used to be additionally learned very early that many easy homes of and operations on photograph subsets may be prolonged to fuzzy subsets; for instance, the vintage paper on fuzzy units through Zadeh  mentioned the "set algebra" of fuzzy units (using sup for union and inf for intersection), and prolonged the defmition of convexity to fuzzy units. those and comparable principles allowed a number of the equipment of picture research to be generalized to fuzzy photograph elements. For are cent overview on geometric description of fuzzy units see, e. g. , . Fuzzy tools also are beneficial in picture processing and coding, the place studying approaches could be very important in identifying the parameters of filters, quantizers, etc.
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Extra resources for Soft Computing for Image Processing
Peng, S. and Lucke, 1. (1994): Fuzzy filtering for mixed noise removal during image processing. In: Proc. 89-93. Pitas, I. and Venetsanopoulos, A. N. Kluwer Academic Publisher. Plataniotis, K. , Androutsos, D. and Venetsanopoulos, A. N (1995): Color image processing using fuzzy vector directional filters. In Proc. IEEE NSIP-95, Neos Marmaras, Halkidiki, Greece, pp. 535-538. 43 Russo, F. (1997): Nonlinear Filtering of Noisy Images Using Neuro-Fuzzy Operators. In: Proc. IEEE 1997 Int. Conf. 412-415.
In these test images, each image contains edges that meet other edges, or edges with high curvature. Also, in this case, step edges were evaluated instead of sloping 52 (al) (bl) (el) (dl) (a2) (b2) (e2) (d2) (a3) (b3) (03) (d3) Fig. 5. Smoothing images with high curvature edges: (a) original images, (b) with random added noise, (c) fuzzy gaussian smoothing, (d) best gaussian smoothing edges. Once again, we added noise and smooth with Gaussian and fuzzy Gaussian filters. Table 3. Results of fuzzy Gaussian blurring Image added error per pixel SNR error after Gauss per pixel error after fuzzy Gauss SNRimp.
SPIE : Visual Communication and Image Processing, 1001:1070-1077. 47. Luttrell S. P. (1989) Image compression using a multilayer neural network. Pattern Recognition Letters, 10:1-7. 48. Dony R. , Haykin S. (1995) Neural network approaches to image compression. Pmc. of the IEEE, 8:288-303. 49. Chen C. , Tsao E. , Lin W. C. (1991) Medical image segmentation by a constraint satisfaction neural network. IEEE Transactions on Nuclear Science, 38:678-686. 50. Manjunath B. , Chellappa R. (1990) Stochastic and deterministic networks for texture segmentation.