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Image Processing Techniques and Neuro-computing Algorithms in Computer Vision

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.

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

APA Style

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.

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/

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/

Copyright © 2021 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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