Automated models of visual information processing

Authors

  • Mohylnyi Oleksandr

DOI:

https://doi.org/10.34185/1562-9945-4-147-2023-09

Keywords:

image processing, pattern recognition, object detection, machine learning, image classification, feature extraction.

Abstract

The article presents a study devoted to the development and research of an automated model of visual information processing. The goal of the research was to create a comprehen-sive model capable of automatically processing and analyzing various forms of visual data, such as images and videos. The model is developed on the basis of a combined approach that combines various algorithms and methods of visual information processing. The literature review conducted within the scope of this study allowed us to study the existing methods and algorithms for visual information processing. Various image processing approaches were analyzed, including segmentation, pattern recognition, object classification and detection, video analysis, and other aspects. As a result of the review, the advantages and limitations of each approach were identified, as well as the areas of their application were determined. The developed model showed high accuracy and efficiency in visual data processing. It can suc-cessfully cope with the tasks of segmentation, recognition and classification of objects, as well as video analysis. The results of the study confirmed the superiority of the proposed model. Potential applications of the automated model are considered, such as medicine, robotics, security, and many others. However, limitations of the model such as computational resource requirements and quality of input data are also noted. Further development of this research can be aimed at optimizing the model, adapting it to specific tasks and expanding its func-tionality. In general, the study confirms the importance of automated models of visual infor-mation processing and its important place in modern technologies. The results of the research can be useful for the development of new systems based on visual data processing and con-tribute to progress in the field of computer vision and artificial intelligence.

References

Ajeet Ram Pathak. Application of Deep Learning for Object Detection / Ajeet Ram Pathak, Manjusha Pandey, Siddharth Rautaray // International Conference on Computational Intelligence and Data Science (ICCIDS 2018). – 2018. – P. 1706-1717.

Christ P.F. Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields / P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi, W. H. Sommer, S.-A. Ahmadi, B. H. Menze // MICCAI, Cham. – 2016. – P. 415–423.

Gonal J. S. Morphological Segmentation of the Brain Tumors by Using Image Processing and Lab-VIEW /J. S. Gonal, V. V. Kohir // X International Journal of Engineering Research & Technology (IJERT) – Volume 4, Special Issue. – 2015. –

P. 334-341.

Hanchi Liu. Application of Deep Learning-Based Object Detection Techniques in Fish Aquaculture: A Review / Hanchi Liu, Xin Ma, Yining Yu, Liang Wang, Lin Hao // Journal of Marine Science and Engineering. – 2023 – №11(4):867.

Kapil Kumar Gupta. A Comparative Study of Medical Image Segmentation Techniques for Brain Tumor Detection / Kapil Kumar Gupta, Namrata Dhanda, Upendra Kumar // 4th International Conference on Computing Communication and Automation (ICCCA). – 2018. – P. 1-4.

Krasilenko Vladimir G. Application of nonlinear correlation functions and equivalence models in ad-vanced neuronets / Vladimir G. Krasilenko, Oleg K. Kolesnitsky, Anatoly K. Bogukhvalsky // Proceedings of SPIE – Vol. 3317. – 2017. – P. 211-222. 444

Pushpajit A. Khaire. An Overview Of Image Segmentation Algorithms / Pushpajit A. Khaire, Nileshsingh V. Thakur // International Journal of Image Processing and Vision Science. – 2013. – Vol. 1: Iss. 3 , Article 1. – P. 150-156.

DOI: 10.47893/IJIPVS.2013.1028

Rituparna Sarma. A comparative study of new and existing segmentation techniques / Rituparna Sarma,Yogesh Kumar Gupta // IOP Conference Series: Materials Science and Engineering, Volume 1022, 1st International Conference on Computational Research and Data Analytics (ICCRDA). – 2020. – P.1022-1033.

DOI 10.1088/1757-899X/1022/1/012027

Ronneberger O. U-net: Convolutional networks for biomedical image segmentation / Ronneberger O., Fischer P., Brox T. // MICCAI, Vol. 9351. – 2015. –

P. 234–241.

Sharmila T. Impact of applying pre-processing techniques for improving classification accuracy / Sharmila T., Ramar K., Thangaswamy Sree Renga Raja // Signal, Image and Video Processing. – 2014. – №8(1). – P. 149-157.

DOI:8. 10.1007/s11760-013-0505-7.

Published

2023-11-13