Exploring Computer Vision's Deep Learning and Machine Learning Techniques

International Journal of Computer Science and Engineering

© 2023 by SSRG - IJCSE Journal

Volume 10 Issue 2

Year of Publication : 2023

Authors : R. Surendiran

10.14445/23488387/IJCSE-V10I2P101

How to Cite?

R. Surendiran, "Exploring Computer Vision's Deep Learning and Machine Learning Techniques," SSRG International Journal of Computer Science and Engineering , vol. 10,  no. 2, pp. 1-9, 2023. Crossref, https://doi.org/10.14445/23488387/IJCSE-V10I2P101

Abstract:

Due to the obtainability and approachability of vast volumes of data generated via devices and the net, computer applications have undergone a fast shift in recent years from unassuming data dispensation to machine learning with the passing of the period. Western countries have demonstrated prodigious attention to ML, CV, and pattern acknowledgement by hosting sessions, conferences, group discussions, researching, and applying their findings in the real world. This Research on ML applications in CV examines, analyzes, and predicts potential developments. The study identified unsupervised, supervised, and semi-supervised machine learning algorithms as the three main categories. Neural networks, k-means clusters, and sustenance vector machines are some of the most frequently used approaches. Object documentation, object organization, and info extraction from images, graphic credentials, and videos are some of the most current machine learning submissions in computer visualization. Tensor tide, the Faster-RCNN-Inception-V2 prototypical, and the Eunectes murinus package growth atmosphere were also used to recognize automobiles and people in photographs.

Keywords:

Image dispensation, Article identification, Computer vision, Artificial astuteness, Image classification, Neuronal networks.

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