Soft Computing Techniques for Various Image Processing Applications: A Survey

Rahul Kher; Heena Kher

doi:doi:10.11648/j.jeee.20200803.11

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Soft Computing Techniques for Various Image Processing Applications: A Survey

Rahul Kher, Heena Kher

Published in Journal of Electrical and Electronic Engineering (Volume 8, Issue 3)

Received: 26 April 2020 Accepted: 22 May 2020 Published: 20 June 2020

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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.

Published in	Journal of Electrical and Electronic Engineering (Volume 8, Issue 3) This article belongs to the Special Issue Soft Computing Methods for Electrical and Electronics Engineering Applications
DOI	10.11648/j.jeee.20200803.11
Page(s)	71-80
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

Soft Computing, Image Processing, Fuzzy Logic, Neural Networks, Medical Images

References

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Rahul Kher, Heena Kher. (2020). Soft Computing Techniques for Various Image Processing Applications: A Survey. Journal of Electrical and Electronic Engineering, 8(3), 71-80. https://doi.org/10.11648/j.jeee.20200803.11

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ACS Style

Rahul Kher; Heena Kher. Soft Computing Techniques for Various Image Processing Applications: A Survey. J. Electr. Electron. Eng. 2020, 8(3), 71-80. doi: 10.11648/j.jeee.20200803.11

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AMA Style

Rahul Kher, Heena Kher. Soft Computing Techniques for Various Image Processing Applications: A Survey. J Electr Electron Eng. 2020;8(3):71-80. doi: 10.11648/j.jeee.20200803.11

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@article{10.11648/j.jeee.20200803.11,
  author = {Rahul Kher and Heena Kher},
  title = {Soft Computing Techniques for Various Image Processing Applications: A Survey},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {8},
  number = {3},
  pages = {71-80},
  doi = {10.11648/j.jeee.20200803.11},
  url = {https://doi.org/10.11648/j.jeee.20200803.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20200803.11},
  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.},
 year = {2020}
}

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TY  - JOUR
T1  - Soft Computing Techniques for Various Image Processing Applications: A Survey
AU  - Rahul Kher
AU  - Heena Kher
Y1  - 2020/06/20
PY  - 2020
N1  - https://doi.org/10.11648/j.jeee.20200803.11
DO  - 10.11648/j.jeee.20200803.11
T2  - Journal of Electrical and Electronic Engineering
JF  - Journal of Electrical and Electronic Engineering
JO  - Journal of Electrical and Electronic Engineering
SP  - 71
EP  - 80
PB  - Science Publishing Group
SN  - 2329-1605
UR  - https://doi.org/10.11648/j.jeee.20200803.11
AB  - 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.
VL  - 8
IS  - 3
ER  -

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Author Information

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

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Plain Text BibTeX RIS

APA Style

Rahul Kher, Heena Kher. (2020). Soft Computing Techniques for Various Image Processing Applications: A Survey. Journal of Electrical and Electronic Engineering, 8(3), 71-80. https://doi.org/10.11648/j.jeee.20200803.11

Copy | Download

ACS Style

Rahul Kher; Heena Kher. Soft Computing Techniques for Various Image Processing Applications: A Survey. J. Electr. Electron. Eng. 2020, 8(3), 71-80. doi: 10.11648/j.jeee.20200803.11

Copy | Download

AMA Style

Rahul Kher, Heena Kher. Soft Computing Techniques for Various Image Processing Applications: A Survey. J Electr Electron Eng. 2020;8(3):71-80. doi: 10.11648/j.jeee.20200803.11

Copy | Download

@article{10.11648/j.jeee.20200803.11,
  author = {Rahul Kher and Heena Kher},
  title = {Soft Computing Techniques for Various Image Processing Applications: A Survey},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {8},
  number = {3},
  pages = {71-80},
  doi = {10.11648/j.jeee.20200803.11},
  url = {https://doi.org/10.11648/j.jeee.20200803.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20200803.11},
  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.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Soft Computing Techniques for Various Image Processing Applications: A Survey
AU  - Rahul Kher
AU  - Heena Kher
Y1  - 2020/06/20
PY  - 2020
N1  - https://doi.org/10.11648/j.jeee.20200803.11
DO  - 10.11648/j.jeee.20200803.11
T2  - Journal of Electrical and Electronic Engineering
JF  - Journal of Electrical and Electronic Engineering
JO  - Journal of Electrical and Electronic Engineering
SP  - 71
EP  - 80
PB  - Science Publishing Group
SN  - 2329-1605
UR  - https://doi.org/10.11648/j.jeee.20200803.11
AB  - 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.
VL  - 8
IS  - 3
ER  -

Copy | Download