Using OpenCV for Machine Learning in Real Time Computer Vision and Image Processing
S Pradeep1, Yogesh Kumar Sharma2
1S Pradeep, Research Scholar, Download PDF Computer Science Engineering, JJT University, Jhunujhunu, Rajasthan, India.
2Dr Yogesh Kumar Sharma, Associate Professor, Download PDF Computer Science Engineering, JJT University, Jhunujhunu, Rajasthan, India.
Manuscript received on 20 April 2019 | Revised Manuscript received on 27 May 2019 | Manuscript published on 30 May 2019 | PP: 1846-1848 | Volume-8 Issue-1, May 2019 | Retrieval Number: A1031058119/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Computer Vision and Digital Image Processing is one of the emerging domain now days in different applications including Face Recognition, Biometric Validations, Internet of Things (IoT), Criminal Investigation, Signature Patterns Detection in Banking, Digital Documents Analysis, Smart Tags based Vehicles for recognition at Toll Plaza and many others. All these applications use image and real time video processing so that the live capturing of multimedia impression can be capture for detailed analysis and predictions. This paper underlines the use of OpenCV as the effective framework for the assorted application domains with the specific scenarios associated with the image forensics. In addition, the manuscript is presenting the cavernous view of the OpenCV to identify the deep images with the analytics patterns for the predictions in the hidden patterns.
Index Terms: Computer Vision, Deep Learning, Machine Learning, OpenCV
Scope of the Article: Deep Learning