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Image Processing Techniques and Neuro-computing Algorithms in Computer Vision

Received: 30 July 2021     Accepted: 16 August 2021     Published: 12 October 2021
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Abstract

Computer vision is a multidisciplinary field that cannot be separated with image processing techniques and Neuro-Computing specifically Deep Learning (DL) algorithms, in recent time DL techniques enable computer vision to understand the content of an image, moreover, it is working hand in hand with image processing techniques because image preprocessing are essential components in digital image analysis. Therefore, the remarkable advancement recorded by computer vision today such as in remote sensing, security, medical imaging and robotics etc. The aim of this research work was to explored the technical and theoretical contributions of image processing techniques and DL algorithms to computer vision. A systematic method of literature review was adapted. Basic image processing techniques such as standardization, denoising, filtering, and segmentation are clearly explored, concept of DL algorithms are briefly discussed, recent reviewed articles (from 2018 to date) are obtained from top journals in computer vision thus; IEEE, Elsevier and ISPR and tabulated as a major source of information for this work. We have shown some of the software’s used for the implementation of deep learning researches in computer vision. Finally we concludes and give recommendations based on our findings.

Published in Advances in Networks (Volume 9, Issue 2)
DOI 10.11648/j.net.20210902.12
Page(s) 33-38
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), 2021. Published by Science Publishing Group

Keywords

Computer Vision, Deep Learning, Object Detection, Neuro-computing, Image Processing, Filtering

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    Ibrahim Goni, Asabe Sandra Ahmadu, Yusuf Musa Malgwi. (2021). Image Processing Techniques and Neuro-computing Algorithms in Computer Vision. Advances in Networks, 9(2), 33-38. https://doi.org/10.11648/j.net.20210902.12

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    ACS Style

    Ibrahim Goni; Asabe Sandra Ahmadu; Yusuf Musa Malgwi. Image Processing Techniques and Neuro-computing Algorithms in Computer Vision. Adv. Netw. 2021, 9(2), 33-38. doi: 10.11648/j.net.20210902.12

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    AMA Style

    Ibrahim Goni, Asabe Sandra Ahmadu, Yusuf Musa Malgwi. Image Processing Techniques and Neuro-computing Algorithms in Computer Vision. Adv Netw. 2021;9(2):33-38. doi: 10.11648/j.net.20210902.12

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  • @article{10.11648/j.net.20210902.12,
      author = {Ibrahim Goni and Asabe Sandra Ahmadu and Yusuf Musa Malgwi},
      title = {Image Processing Techniques and Neuro-computing Algorithms in Computer Vision},
      journal = {Advances in Networks},
      volume = {9},
      number = {2},
      pages = {33-38},
      doi = {10.11648/j.net.20210902.12},
      url = {https://doi.org/10.11648/j.net.20210902.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.net.20210902.12},
      abstract = {Computer vision is a multidisciplinary field that cannot be separated with image processing techniques and Neuro-Computing specifically Deep Learning (DL) algorithms, in recent time DL techniques enable computer vision to understand the content of an image, moreover, it is working hand in hand with image processing techniques because image preprocessing are essential components in digital image analysis. Therefore, the remarkable advancement recorded by computer vision today such as in remote sensing, security, medical imaging and robotics etc. The aim of this research work was to explored the technical and theoretical contributions of image processing techniques and DL algorithms to computer vision. A systematic method of literature review was adapted. Basic image processing techniques such as standardization, denoising, filtering, and segmentation are clearly explored, concept of DL algorithms are briefly discussed, recent reviewed articles (from 2018 to date) are obtained from top journals in computer vision thus; IEEE, Elsevier and ISPR and tabulated as a major source of information for this work. We have shown some of the software’s used for the implementation of deep learning researches in computer vision. Finally we concludes and give recommendations based on our findings.},
     year = {2021}
    }
    

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  • TY  - JOUR
    T1  - Image Processing Techniques and Neuro-computing Algorithms in Computer Vision
    AU  - Ibrahim Goni
    AU  - Asabe Sandra Ahmadu
    AU  - Yusuf Musa Malgwi
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    N1  - https://doi.org/10.11648/j.net.20210902.12
    DO  - 10.11648/j.net.20210902.12
    T2  - Advances in Networks
    JF  - Advances in Networks
    JO  - Advances in Networks
    SP  - 33
    EP  - 38
    PB  - Science Publishing Group
    SN  - 2326-9782
    UR  - https://doi.org/10.11648/j.net.20210902.12
    AB  - Computer vision is a multidisciplinary field that cannot be separated with image processing techniques and Neuro-Computing specifically Deep Learning (DL) algorithms, in recent time DL techniques enable computer vision to understand the content of an image, moreover, it is working hand in hand with image processing techniques because image preprocessing are essential components in digital image analysis. Therefore, the remarkable advancement recorded by computer vision today such as in remote sensing, security, medical imaging and robotics etc. The aim of this research work was to explored the technical and theoretical contributions of image processing techniques and DL algorithms to computer vision. A systematic method of literature review was adapted. Basic image processing techniques such as standardization, denoising, filtering, and segmentation are clearly explored, concept of DL algorithms are briefly discussed, recent reviewed articles (from 2018 to date) are obtained from top journals in computer vision thus; IEEE, Elsevier and ISPR and tabulated as a major source of information for this work. We have shown some of the software’s used for the implementation of deep learning researches in computer vision. Finally we concludes and give recommendations based on our findings.
    VL  - 9
    IS  - 2
    ER  - 

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Author Information
  • Department of Computer Science, Faculty of Physical Science, Modibbo Adama University, Yola, Nigeria

  • Department of Computer Science, Faculty of Physical Science, Modibbo Adama University, Yola, Nigeria

  • Department of Computer Science, Faculty of Physical Science, Modibbo Adama University, Yola, Nigeria

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