Image understanding as a second course in AI

Abstract

This paper describes the development and structure of a second course in artificial intelligence that was developed to meet the needs of upper-division undergraduate and graduate computer science and computer engineering students. These students already have a background in either computer vision or artificial intelligence, and desire to apply that knowledge to the design of algorithms that are able to automate the process of extracting semantic content from either static or dynamic imagery. Theory and methodology from diverse areas were incorporated into the course, including techniques from image processing, statistical pattern recognition, knowledge representation, multivariate analysis, cognitive modeling, and probabilistic inference. Students read selected current literature from the field, took turns presenting the selected literature to the class, and participated in discussions about the literature. Programming projects were required of all students, and in addition, graduate students were required to propose, design, implement, and defend an image understanding project of their own choosing. The course served as preparation for and an incubator of an active research group.