Volume 8, Issue 3, June 2020, Page: 71-80
Soft Computing Techniques for Various Image Processing Applications: A Survey
Rahul Kher, Department of Electronics & Communication Engg, G H Patel College of Engineering & Technology, Vallabh Vidyanagar, India
Heena Kher, Department of Electronics & Communication Engg, A D Patel Institute of Technology, New Vallabh Vidyanagar, India
Received: Apr. 26, 2020;       Accepted: May 22, 2020;       Published: Jun. 20, 2020
DOI: 10.11648/j.jeee.20200803.11      View  290      Downloads  215
Abstract
Soft computing techniques have found numerous applications in various domains of image processing and computer vision. This paper represents a survey on various soft computing methods’- fuzzy logic, neural network, neuro-fuzzy systems, genetic algorithm, evolutionary computing, support vector machine etc. - applications in various image processing areas. There are numerous applications of SC ranging from industrial automation to agriculture and from medical imaging to aerospace engineering, but this paper deals with the relevance and feasibility of soft computing tools in the area of image processing, analysis and recognition. The techniques of image processing stem from two principal applications namely, improvement of pictorial information for human interpretation and processing of scene data for automatic machine perception. The different tasks involved in the process include enhancement, filtering, noise reduction, segmentation, contour extraction, skeleton extraction etc. Their ultimate aim is to make understanding, recognition and interpretation of the images from the processed information available from the image pattern. There are many hybridized approaches like neuro-fuzzy system (NFS), fuzzy-neural network (FNN), genetic-fuzzy systems, neuro-genetic systems, neuro-fuzzy-genetic system exist for various image processing applications. Tools like genetic algorithms (GAs), simulated annealing (SA), and tabu search (TS) etc. have been incorporated with soft computing tools for applications involving optimization.
Keywords
Soft Computing, Image Processing, Fuzzy Logic, Neural Networks, Medical Images
To cite this article
Rahul Kher, Heena Kher, Soft Computing Techniques for Various Image Processing Applications: A Survey, Journal of Electrical and Electronic Engineering. Special Issue: Soft Computing Methods for Electrical and Electronics Engineering Applications. Vol. 8, No. 3, 2020, pp. 71-80. doi: 10.11648/j.jeee.20200803.11
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Kurhe A. B., Satonkar S. S., Khanale P. B. and Shinde Ashok, “Soft computing and its applications”, BIOINFO Soft Computing, Volume 1, Issue 1, 2011, pp-05-07.
[2]
Sankar K. Pal, Ashish Ghosh, and Malay K. Kundu, “Soft Computing for Image Processing”, Part of the Springer book series Studies in Fuzziness and Soft Computing, 2000, Volume 42, ISBN: 978-3-7908-2468-1. DOI: https://doi.org/10.1007/978-3-7908-1858-1.
[3]
J. Bezdek and S. K. Pal Editors. Fuzzy Models for Pattern Recognition. IEEE Press, Boca Raton, 1992.
[4]
Y. H. Pao. Adaptive Pattern Recognition and Neural Networks. Addison Wesley, New York, 1989.
[5]
S. Newton, S. Pemmaraju, and S. Mitra. Adaptive fuzzy leader clustering of complex data sets in pattern recognition. IEEE Trans. Neural Networks, 3 (5): 794-800, 1992.
[6]
Y. Kim and S. Mitra. An adaptive integrated fuzzy clustering model for pattern recognition. Fuzzy Sets and Systems, 65: 297-310, 1994.
[7]
Cannon, R. 1., Dave, R., Bezdek, J. C, and Trivedi, M. (1986) Segmentation of a thematic mapper image using the fuzzy c-means clustering algorithm. IEEE Transactions on Geoscience and Remote Sensing, 24, 400-408.
[8]
Laprade, R. H. (1988) Split-and-merge segmentation of aerial photographs. Computer Vision, Graphics and Image Processing, 44, 77-86.
[9]
MandaI, D. P., Murthy, C. A and Pal, S. K. (1994) Utility of multiple choices is detecting ill-defined roadlike structures. Fuzzy Sets and Systems, 64, 213-228.
[10]
Sahasrabudhe, S. C. and Dasgupta, S. C. (1992) A valley-seeking threshold selection technique. In Computer Vision and Image Processing, L. Shapiro and A. Rosenfeld eds., (Boston: Academic Press), 55-65.
[11]
Barzohar, M. and Cooper, D. B. (1993) Automatic finding of main roads in aerial images by using geometric-stochastic models and estimation. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 459-464.
[12]
Hu, J., Sakoda, B. and Pavlidis, T. (1992) Interactive road finding for aerial images. In Applications of Computer Vision, 56-63.
[13]
Zlotnick, A. and Carnine (Jr.), P. D. (1993) Finding road seeds in aerial images. Image Understanding, 57, 307-330.
[14]
I. Yamasaki, M. Hasegawa, S. Ikarashi and S. Okada (1992) Data compression of digital images with grey level using triangular plane patches. Trans. of IEICE, Vol. J75-D-II, No. 6, 1038-1047.
[15]
Bhanu, B. (1986): Automatic target recognition: state of the art survey. IEEE Trans. Aerospace Elect. Systems, 22 (4), 364-379.
[16]
Hart, P. E. (196S): The Condensed Nearest Neighbour Rule. IEEE Trans. on Information Theory IT-14, 515-516.
[17]
Chern Hong Lim, Ekta Vats and Chee Seng Chan, “Fuzzy human motion analysis: A review”, Pattern Recognition, Vol. 48, Issue 5, May 2015, 1773-1796.
[18]
Oblander, R., K. Price, and D. R. Reddy (1978). Picture segmentation using a recursive region splitting method. Comput. Graphics Image Processing 8, 313-333.
[19]
Faugeras, O. D. and K. E. Price (1981). Semantic description of aerial images using stochastic labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence 3, 633-642.
[20]
Shariat, H. and K. E. Price (1990). Motion estimation with more than two images. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 417-434.
[21]
Grossberg, S. and M. E. Rudd (1989). A neural architecture for visual motion perception: Group and element apparent motion. Neural Networks 2 (6), 421-450.
[22]
Jordan, M. J. (1986). Attractor dynamics and parallelism in a connectionist sequential machine. In Proc. Eighth Ann Conf. of Cognitive Science Society, pp. 531-546.
[23]
Ellman, J. (1990). Finding structure in time. Cognitive Science 14, 179-211.
Browse journals by subject