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 |
Computer Vision, Deep Learning, Object Detection, Neuro-computing, Image Processing, Filtering
| [1] | Sevo L. and Avramove, (2016). A Convolutional Neutral Networks Based Automatic object Detection on Aerial images. IEEE Geoscience. Remote Sensing letter 2016 13, 740-74. |
| [2] | Jiao L., F. Zhang, F. Liu, S. Yang, L. Li, Z. Feng, R. Qu, (2019). A survey of deep learning- based object detection, arXiv: 1907.09408. |
| [3] | Abhishek G., Alagan A., Ling G., Ahmed S. K. (2021). Deep learning for object detection and Scene perception in self-driving cars: Survey, challenges, and open issues. Science Direct Array 10 (2021) 100057. |
| [4] | Cheng, P. Zhou, and J Han, “learning rotation-invariant convolution neutral networks for object detection in VHR optical remote sensing images, “IEEE Trans. Geoscience and Remote Sensing, 54 (12), 7405-7415, 2016. |
| [5] | Sevo and A. Avramovic, (2016). Convolutional Neutral Network based automatic object detection on arial images IEEE Geoscience and remote sensing letter. vol. 13, no. 5, pp. 740-744, 2016. |
| [6] | Girshick, J. Donahue, T. Darrell, and J. Malik, (2016) Region-based convolutional networks for Accurate object detection and sematic segmentation, “IEEE Trans. Pattern Anal mach. Intell. vol. 38, no. l, pp 142-158, 2016. |
| [7] | Ross G., Jeff D., Trevor D., Jitendra M., (2014). Rich feature hierarchies for accurate object detection and semantic segmentation, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 580–587. |
| [8] | Joseph R., Santosh D., Ross G., Ali F., (2016) You only look once: Unified, real-time object detection, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 779–788. |
| [9] | Lin T. Y., Dollár P., Girshick R., K. He, B. Hariharan, and S. Belongie, (2017). Feature pyramid networks for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125, 2017. |
| [10] | Singh S. and L. S. Davis, (2018). An analysis of scale invariance in object detection snip,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3578–3587. |
| [11] | Romero, Gatta, Camps,-valls, (2016). Unsupervised deep feature extraction for remote sensing image classification. IEEE Transaction of Geoscience and Remote sensing 54 (3), 1349-1362. |
| [12] | Chen, Y., Lin Z., Zhao X., Wang G., Gu Y. (2014). Deep learning-based classification of hyper spectral data. IEEE J. sel. Top. Appl. Earth Obs. Remote Sensing. 7 (6), 2094-2107. |
| [13] | Marmanis D., Datcu M., Esch T. and Stilla U., (2016). Deep learning earth observation classification using ImageNet pre-trained networks. IEEE Geoscience and remote sensing letter 13 (1). 105-109. |
| [14] | Kussual, N., Lavreniuk, M., Shelestov S. (2017). A deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience Remote sensing Letter. 14, 778-782. |
| [15] | Scott G., M. England, W. Starms R. Marcum, and C. Davis, (2017). Training deep Convolution neural networks for land-cover classification of high-resolution Imagery”, IEEE geosciences and Remote sensing letters, Vol. 14, no. 4, 2017. |
| [16] | Goodin, D. g., Anibas, k. L., Bezymennyi, M. (2015). Mapping land cover and land use from object–based classification: an example from a complex agricultural Landscape. Transaction of Geosciences and Remote Sensing. 63, 382-396. |
| [17] | Long, E. Shelhamer, and T. Darrell, (2015). Fully convolutional networks for semantic Segmentation, in proceeding. IEEE International. Conf. computer vision and pattern recognition (CVPR), 3431-3440. |
| [18] | H. Noh, S. Hong and B. Han, (2015) Learning de-convolutional network for semantic segmentation, In Proceeding IEEE international conference and computer vision (ICCV), 2015, pp. 1520-1528. |
| [19] | Zhao J., Feng J., Wu X. and Yan S. (2017). A Survey on deep learning-based fine-grained Objection classification and semantic segmentation. International Journal of Automat. Comput. 14 (2), 119-135. |
| [20] | Duan, F. Liu, L., Jiao, P. Zhao, and L., Zhang, (2017). SAR image segmentation based on convolutional- wavelet neural network and Markov random field, Pattern Recognition, 64, 255-267. |
| [21] | Kampffmeyer, A. B. Salberg, and R. Jenssen, (2016) semantic segmentation of small objects and Modeling of uncertainty in urban remote sensing image using images using deep convolutional neutral networks,” in Proceeding IEEE int. conf. computer vision and pattern recognition (CVPR) workshop, 1-9. |
| [22] | Cheng G. and Han J. (2016). A survey on object detection in optical remote sensing images. ISPRS Journal of Photogrammetry and Remote sensing. 117, 11-28. |
| [23] | Hu, G. S. Xia, Z. Wang, X. Huang, L. Zhang, and H. Sun, (2015). Unsupervised features learning via spectral clustering of multidimensional patches for remotely sensed scene classification, IEEE Journal select. Topics appl. Earth observed Remote Sensing, 8 (5) 203. |
| [24] | Zou, L. Ni, T. Zhang, and Q. Wang, (2015) Deep learning based feature selected for remote sensing scene classification, IEEE Geoscience remote sensing letter. 12 (11). 2321-2325, 2015. |
| [25] | Hu, G-S. Xia, J. Hu, and L. Zhang, (2015). Transferring deep convolutional neutral networks for the scene classification of high-resolution remote sensing imagery, Remote Sensing, 7 (11) 14680-14707. |
| [26] | Zhang, B. Du, and L. Zhang, (2015). Scene classification via a gradient boosting random convolutional network framework, IEEE Transaction Geoscience and Remote sensing. 54 (3), 1793-1802. |
| [27] | Cheng, G.; Ceyuan, Y.; Xiwen, Y.; Guo, L.; Junwei, H. (2018). When deep learning meets Metric learning: remote sensing image scene classification via learning discriminative CNNs. IEEE Transaction Geoscience Remote Sensing. 56, 2811–2821. |
| [28] | Cheng, G., Ma, C., Zhou, P., Yao, X., Han, J. (2016). Scene classification of high resolution remote sensing images using convolutional neural networks. In Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 10–15 July, 2016, 767–770. |
| [29] | Boualleg, Y., Farah, M. (2018). Enhanced interactive remote sensing image retrieval with scene Classification convolutional neural networks model. In Proceedings of the IGARSS 2018—2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018. 4748–4751. |
| [30] | Ye, F.; Xiao, H.; Zhao, X.; Dong, M.; Luo, W.; Min, W. (2018). Remote Sensing Image Retrieval Using Convolutional Neural Network Features and Weighted Distance. IEEE Geoscience Remote Sensing Letter 15, 1535–1539. |
| [31] | Zhou, W.; Newsam, S.; Li, C.; Shao, Z. (2017). Learning Low Dimensional Convolutional Neural networks for high-resolution remote sensing image retrieval. Remote Sensing, 9, 489. |
| [32] | Li, W., Fu, H., Le, Y., Peng, G., Feng, D., Li, C., Clinton, N. (2016). Stacked auto encoder based deep learning for remote-sensing image classification: a case study of African land-cover mapping. International Journal of Remote Sensing. 37, 5632–5646. |
| [33] | Zhong, B. Yang, G. Huang, F. Zhong and Z. Chen, (2016). Remote sensing image fusion with Convolution neutral network, sensing imaging. 17 (1) 2016. |
| [34] | Xiao X. Z., Devis T., Linchao M., Gui-Song X., Liangpei Z., Feng X. and Frieddrich F. (2017). Deep learning in Remote Sensing: A Comprehensive Reviw and list of resources. IEEE Geoscience and Remote Sensing Magazine Doi 10.1109MGRS. |
| [35] | Han, D. Zhangm G. Cheng, L. Guo, and J. Ren, (2015). Object Detection in Optical Remote sensing image based on weekly supervised learning and high-level feature learning,” IEEE Transaction Geoscience and remote sensing, 53 (6), 3325-3337. |
| [36] | Milan, S., Vaclav H. and Roger B., (2015). Image Processing, Analysis and Machine Vision Cengage Learning 200 First Stamford Place, 4th Floor Stamford, CT 06902 USA. |
| [37] | Athanasios V., Kolas D., Anastasios D. and Eftychios P., (2018). Deep learning for computer vision: A brief review. Hindawi Computational intelligence and Neuroscience. |
| [38] | Srivastava N. and Salakhutdinov R., (2014). Multimodal learning with deep Boltzmann machines, Journal of Machine Learning Research. 15, 2949–2980. |
| [39] | Arshitha F. and Biju K. S. (2020). Accurate detection of building from satellite images using CNN. IEEE. |
| [40] | Kadhim, Mohammed and Abed, Mohammed, (2020). Convolution neural network for Satellite Image Classification. |
| [41] | Imamoglu, N., Martinez G., Pascual, hamaguchi, Ryuhei, Sakurada, Ken and Nakamura, R. (2018). Exploring recurrent and feedback CNNs for Multi-Spectral Satellite image classification Proceeding in Computer Science. 140, 162-169. |
| [42] | Deepthi, S., Sandeep K. and Suresh L. (2021). Detection and Classification of Objects in Satellite Images using Custom CNN. International Journal of Engineering Research & Technology. 10 (6), 629-635. |
| [43] | Ding, J., Chen B., Liu H., and Huang M., (2017). Convolutional neural network with data augmentation for SAR target recognition, IEEE Geoscience and Remote Sensing Letter, 13 (3), 364-368. |
| [44] | Gao, F., Huang, T., Wang, J., Sun, J., Hussain, A., Yang, E. (2017). Dual-branch deep convolution neural network for Polari metric SAR image classification. Applied Science. 7, 447. |
| [45] | Cheng, G. Ceyuan, Y. Xiwen, Y.; Guo, L. Junwei, H. (2018). When deep learning meets Metric learning: remote sensing image scene classification via learning discriminative CNNs. IEEE Transaction Geoscience Remote Sensing. 56, 2811–2821. |
| [46] | Zhou, W., Deng, X., Shao, Z. (2018). Region convolutional features for multi-label remote sensing image retrieval. arXiv arXiv: 180708634. |
| [47] | Zhang, C., Yue, P., Tapete, D., Shangguan, B., wang, M., Wu, Z. (2020). A multi-level context- Guided Classification method with object-based convolutional neutral network for land Cover Classification using very high resolution remote sensing images. International Journal of Application on Earth Observation and Geo-information. 88, 102086. |
| [48] | Zhang, S., Wen, L., Bian, X., Lei, Z., and Li, S. Z. (2018). Single-shot refinement neural network for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4203–4212. |
| [49] | Yu, H., Ma, Y., Wang, L., Zhai, Y., Wang, X. (2017). A landslide intelligent detection method based on CNN and RSG_R. IEEE international conference on mechatronics and Automation (ICMA). 40-44 6-9. |
| [50] | Mahdianpari, M., Salehi, B., Rezaee, M., Mohammedimanesh, F., Zhang, Y. (2018). Very deep convolutional neutral networks for complex land cover mapping using multi-spectral Remote sensing Imagery. Remote sensing. 10, 1119. |
| [51] | Alshehhi, R., Marpu, P. R., Woon, W. L., and Dalla M. M. (2017). Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks. ISPRS Journal of Photogrammetry and Remote Sensing. 130, 139-149. |
| [52] | Kussul, S. S. (2017). Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote sensing letter. 14 (5). 778-782. |
| [53] | Volpi, M. and Tuia, D. (2017). Dense Semantic Labeling of Sub-Decimeter Resolution Images with Convolutional Neutral networks IEEE Geoscience. Remote sensing letter. 2017, 55, 881-893. |
| [54] | Ren, S., Kaiming H., Ross G., and Jian S. (2017). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.” IEEE transactions on pattern analysis and machine intelligence, 39 (6): 1137–49. |
| [55] | Muhammad K., Ahmad J. and Baik S. W. (2018). Early fire detection using CNN during ssurveillance for effective disaster management. Neurocomputing 288 30-42. |
| [56] | Geng, H. Wang, J. Fan, and X. Ma, (2017). Deep supervised and contractive neural network for SAR image classification. IEEE Transaction Geoscience Remote Sensing 55 (4), 2442-2459, 2017. |
| [57] | Qin, J. Guo, and W. sun, (2017). Object-oriented ensemble classification for polarimetric SAR imagery using restricted Boltzmann machines, Remote Sensing Letter, 8 (3), 204-213, 2017. |
| [58] | Li W., Wu, G., and Du, Q. (2017). Transferred deep learning for anomaly detection in hyper Spectral imagery, IEEE Geoscience and Remote sensing. Letter. doi: 10.1109/LGRS.2657818. |
| [59] | Mou, L., P. G. and Zhu X. X. (2017). Deep recurrent neural networks for hyper spectral image classification. IEEE Transactions on Geosciences and Remote Sensing, 55 (7). |
| [60] | Santara, A., Mani, K., Hatwar, P., Singh, A., Garg, A., Padia, K., and Mitra, P., (2017). Bass net: Band- Adaptive spectral-spatial feature learning neural network for hyper spectral image classification. IEEE Transaction of Geoscience and Remote Sensing, 55 (9), 5293-5301. |
| [61] | Ball J. E., D. T. Anderson, and C. S Chan, (2019). Comprehensive Survey of Deep Learning on Remote Sensing Theories, tool and challenges for the community. Journal of Applied Remote Sensing. 11. (4). 42-56. |
| [62] | Song J., S. Gao, Y. Zhu, And C. Ma, (2019). A Survey of Remote Sensing Image classification Based on CNNs, Big Earth Data, 3 (3). 232-254. |
| [63] | Heydari S. S. and G. Mountrakis. (2019). Meta-analysis of deep-learning in remote sensing: a comprehensive study of mono-temporal classification to support vector machines. ISPRS Journal of Photogrammetric and Remote Sensing. 152, 192-210. |
| [64] | Zhang, L. Zhang, and B. Du, (2020). Deep Learning for Remote Sensing Data: A Technical Tutorial on the state of art, IEEE geosciences and remote sensing magazine, 4 (2). 22-40. |
| [65] | Lu X., Sun H. and Zheng, X. (2019). A feature aggregation convolutional neural network for remote sensing scene classification. IEEE Transaction on Geosciences and Remote Sensing. 57 (10). 78947906. |
| [66] | Paoletti, M., Haut J., Plaza J., and Plaza A. (2019). Deep Learning Classifier for Hyper-Spectral Imaging: a review. ISPRS Journal of Photogrammetric and Remote Sensing, 158, 279-317. |
| [67] | Parikh, H., S. Patel, and Patel, V. (2020). Classification of SAR And POLSAR Images Using Deep Learning: A review of international journal of image and data fusion, 1 (1), 1-32. |
| [68] | Li Y., Zhang H., Hue X., Jiang Y., and Shen Q. (2018). Deep learning for remote sensing image classification. A survey, Wiley Interdisciplinary Review: Data mining and knowledge Discovery 8 (6) 1264. |
| [69] | Pashae M., H. Kamagir, M. J. Strrek, and P. Tissot, (2020). Review and Evaluation of Deep Learning Architecture’s for efficient land cover mapping with UAS hyper-spatial Imagery: a case Study over a wetland, Remote Sensing Image Classification, 12 (6). 959. |
| [70] | Liu, Y., Chen X., Wang Z. J., Ward R. K and Wang X., (2018). Deep learning for pixel-level image fusion: Recent advances and future prospects. Inf. Fusion 42, 158-173. |
| [71] | Laith A., Jinglan Z., Amjad J. H., Ayad A. D., Ye D., Omran A. S., Santamaria J., Mohammed A. F., Muthana A. A. and Laith F., (2021). Review of Deep learning: Concepts, CNN architectures, Challenges, application and feature directions. Journal of Big data Springer open. 8 (53). 1-74. |
| [72] | Xiangwei W., Doyen S., and Steven, C. H. H. (2019). Recent advanced in deep learning for object detection GrXiv. 1908. 03673vi. |
| [73] | Lincheng J., Fan Z., Fan L., Shuyuan Y., Zhixi F. and Rang Q. (2019). A survey of deep learning-Based object detection. IEEE Access Multidisciplinary. 7, 128837-128867. |
| [74] | Payal M., Sharma A. and Singh R., (2020). Deep learning-based object detection in low-altitude UAV datasets: A survey, Image and Vision computing https://doi.org/10.1016/j.imavis.2020.104046. |
| [75] | Jiao and F. Liu, (2019) wishart deep stacking network for the fast polSAR image classification, “IEEE Trans. Image process, 25 (7). 3273-3286. |
| [76] | Vipul, S. and Roohie, N. M. (2020) A comprehensive and systematic look up into deep learning based detection techniques: A review. Computer Science Review 38 Elsevier. 1-29. |
| [77] | Jude D. H., and Vania V. E., (2017). Deep learning for image processing applications IOS Press Asterdam, Berlin and Washington DC. |
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. https://doi.org/10.11648/j.net.20210902.12
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
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
Share This Article
Twitter
Linked In
Facebook
Copy
Download@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}
}
TY - JOUR T1 - Image Processing Techniques and Neuro-computing Algorithms in Computer Vision AU - Ibrahim Goni AU - Asabe Sandra Ahmadu AU - Yusuf Musa Malgwi Y1 - 2021/10/12 PY - 2021 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 -
Email: