The impact of artificial intelligence on computer vision has provided various perspectives and approaches to solving problems of the human visual system. Some of the symbolic processing and knowledge-based techniques implemented in vision systems represent a meaningful extension to the low-level, algorithmic processing which has been emphasized since the advent of the computer vision field. The higher-level processes attempt to capture the essence of visual cognition, specifically by encompassing a model of the visual world and the reasoning processes that manipulate this stored visual knowledge and environmental cues. This thesis includes a discussion of existing computer vision systems surveyed from a high-level perspective. The goal of this thesis is to develop a high-level inference system that implements reasoning processes and utilizes a visual memory model to achieve object recognition in a specific domain. The focus is on symbolically representing and reasoning with high-level knowledge using a frame-based approach. The organization and structuring of domain knowledge, reasoning processes and control and search strategies are emphasized. The implementation utilizes a frame package written in Prolog.
Library of Congress Subject Headings
Computer vision; Expert systems (Computer science); Object-oriented databases; Problem solving--Data processing
Department, Program, or Center
Computer Science (GCCIS)
Wojnowski, Christine, "Reasoning with visual knowledge in an object recognition system" (1990). Thesis. Rochester Institute of Technology. Accessed from
RIT – Main Campus
Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in December 2013.