Image analysis and computer vision for undergraduates

Real hands-on experience can help students gain a better understanding of theoretical problems in image analysis and computer vision and allows them to put into practice and improve their knowledge of digital signal processing, mathematics, statistics, perception and psychophysics. However, important efforts are necessary to enable students to develop a computer vision application because of the lack of extensively tested and well documented software platforms. In this paper, we describe our experience with an open source library addressed at researchers and developers in computer vision, the OpenCV library, its limits when used by students, and how we adapted it for teaching purposes by producing a set of appropriate tutorials. These tutorials help the students reduce the average time for installation and setup from 1 week to 4 hours and help them design an end-to-end image analysis and computer vision project. Finally, we discuss our experience of using this framework for undergraduate as well postgraduate student projects.