Teaching image computation in an upper level elective on robotics
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This article offers a case study of how to teach image computation in an upper level elective course on robotics with a significant number of non-Computer Science majors. The MACS 415 course at the Colorado School of Mines is required for the popular interdisciplinary undergraduate minor in Robotics and AI. It is mandated to provide a broad survey of the artificial intelligence tools available to roboticists, including image computation. Teaching image computation in a robotics elective is challenging both because of the limited time that can be spent on computer vision, and because of the attributes of the students. Non-CS majors typically do not have enough programming experience to program DSP algorithms, yet the students' preferred learning style is "hands-on." In order to reconcile this dilemma, we (1) cover a broad set of topics in class, (2) have several laboratory assignments using khoros, and (3) require the students to complete a robot project involving computer vision. The article summarizes the lessons learned to date, which are expected to be applicable to any course with non-majors involving image computation.
International Journal of Pattern Recognition and Artificial Intelligence
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