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CV Cube: A Kit for Teaching Practical Machine Learning and Computer Vision

by Komal, Arun Kumar Rai
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 14
Year of Publication: 2022
Authors: Komal, Arun Kumar Rai
10.5120/ijca2022922127

Komal, Arun Kumar Rai . CV Cube: A Kit for Teaching Practical Machine Learning and Computer Vision. International Journal of Computer Applications. 184, 14 ( May 2022), 10-12. DOI=10.5120/ijca2022922127

@article{ 10.5120/ijca2022922127,
author = { Komal, Arun Kumar Rai },
title = { CV Cube: A Kit for Teaching Practical Machine Learning and Computer Vision },
journal = { International Journal of Computer Applications },
issue_date = { May 2022 },
volume = { 184 },
number = { 14 },
month = { May },
year = { 2022 },
issn = { 0975-8887 },
pages = { 10-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number14/32390-2022922127/ },
doi = { 10.5120/ijca2022922127 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:21:27.239733+05:30
%A Komal
%A Arun Kumar Rai
%T CV Cube: A Kit for Teaching Practical Machine Learning and Computer Vision
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 14
%P 10-12
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In machine learning there has been strong evidence that data is more important than algorithms; still while teaching machine learning most of the focus is on teaching algorithms. Little to no focus is on teaching about data generation and collection. As a result of this students have no experience about challenges that arise while collecting data sets in real life and ways to overcome those challenges. In this paper, we present CV Cube - a simple kit for teaching machine learning and computer vision techniques that encourages students to work with data that they generate. Hence, students not only learn algorithms but also learn to tackle problems faced while working with new data; therefore gain experience in pre-processing, filtering and noise removal techniques also.

References
  1. Halevy, A., Norvig, P. and Pereira, F.: The unreasonable effectiveness of data. IEEE Intelligent Systems, 24(2), pp.8-12, ( 2009).
  2. Azemi A., D’Imperio N.: Improved approach for delivering an introductory computer science course. 39th IEEE Frontiers in Education Conference, (2009)
  3. Kaur, Pahulpreet, and Bikrampal Kaur.: 2-D geometric shape recognition using canny edge detection technique. IEEE Computing for Sustainable Global Development (INDIACom), 2016 3rd International Conference, pp. 3161-3164, (2016).
  4. Open CV Documentation: http://www.docs.opencv.org.
  5. https://towardsdatascience.com/using-cv-and-ml-to-monitor-activity-while-working-from-home-f59e5302fe67
  6. An Introduction to Neural Network: https://www.inf.ed.ac.uk/
Index Terms

Computer Science
Information Sciences

Keywords

Machine learning computer vision teaching CV Cube

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