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Making image and vision effortless: Learning methodology through the quick and easy design of short case studies
Author(s) -
Mateo Sanguino Tomás de J.,
Castilla Webber Pedro A.
Publication year - 2018
Publication title -
computer applications in engineering education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.478
H-Index - 29
eISSN - 1099-0542
pISSN - 1061-3773
DOI - 10.1002/cae.22003
Subject(s) - artificial intelligence , computer science , robotics , process (computing) , educational robotics , machine vision , image processing , human–computer interaction , machine learning , mathematics education , image (mathematics) , robot , programming language , psychology
Learning on machine vision and image processing generally require high‐level knowledge on techniques, algorithms and programming skills. The educational process is frequently supported by formal lecture approaches assisted by object lessons or lab activities, and project‐based learning methodologies where students engage complex questions, challenges, and problems over a longer period of time. These educational approaches are not effective when applying to learners in robotics study programs or without a programming background where time and motivation are different. To address this concern, this paper presents an educational tool developed to teach the basic principles of machine vision and image processing through the design of short case studies. As the main contribution, the proposed tool allows to shorten the training time required by students—mainly beginners—without the skills in programming and deep understanding of math hidden behind each image operation. This lets to fit theoretical and practical works into short development times. To this end, we conducted an educational experience in robotics subjects with third year students of the computer science and industrial engineering degrees. As a result of this scenario, we statistically compared the teaching and learning issues, the user preferences about the tool and the student academic performance.