How can a course bridge the gap between computer graphics and computer vision? This paper describes an image computation course designed to follow a computer graphics course, building on students' knowledge of coordinate systems and perspective projection. It focuses on image generation, manipulation, matching, and symbolic description. The curriculum covers image generation techniques like ray tracing and introduces fundamental image processing concepts such as Fourier analysis. It also covers computer vision topics like principal components analysis, edge detection, and symbolic feature matching. It prepares the students for advanced work in either computer vision or computer graphics, expanding the students' understanding of these fields. This course aims to equip students with the skills necessary for advanced work in computer vision or computer graphics. The image computation design builds a strong foundation for further exploration of images generation techniques, manipulation, matching, and symbolic description, preparing students for advanced work.
Published in the International Journal of Pattern Recognition and Artificial Intelligence, this paper directly addresses the journal's core topics. The course described focuses on image computation, bridging computer graphics and computer vision. This pedagogical approach and its curriculum are highly relevant to the journal's readership of researchers and educators in these fields.