Teaching image computation in an upper level elective on robotics

This article offers a case study of how to teach image computation in an up per level elective course on robotics with a significant number of non-Comp uter 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 intelli gence tools available to roboticists, including image computation. Teaching image computation in a robotics elective is challenging both becau se 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' prefer red 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 assignm ents using khoros, and (3) require the students to complete a robot project involving computer vision. The article summarizes the lessons learned to d ate, which are expected to be applicable to any course with non-majors invo lving image computation.