Asee peer logo

Undergraduate Computer Vision Curriculum To Complement A Robotics Program

Download Paper |


2004 Annual Conference


Salt Lake City, Utah

Publication Date

June 20, 2004

Start Date

June 20, 2004

End Date

June 23, 2004



Conference Session

Computers in Education Poster Session

Page Count


Page Numbers

9.1330.1 - 9.1330.8

Permanent URL

Download Count


Request a correction

Paper Authors

author page

Randy Broussard

author page

Jenelle Piepmeier

Download Paper |

NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

Undergraduate Computer Vision Curriculum to Complement a Robotics Program Randy P. Broussard, Jenelle Armstrong Piepmeier United States Naval Academy Weapons and System Engineering Department

Abstract This article discusses a computer vision curriculum, including laboratory exercises, which is suitable for undergraduate engineering students. While classroom and laboratory exercises focus on off-line computation, on-line implementation can be achieved with simple equipment such as web-cams. Exercises include a sidewalk or line following exercise utilizing the Hough transform, a face recognition using eigenfaces, barcode reading, handwriting recognition, and sign language recognition. Data-set development for these exercises is also discussed. MATLAB and the Image Processing Toolbox are utilized to allow students to focus on higher-level understanding of commonly available image processing tools. The use of advanced tools allows students to attempt and finish meaningful examples. This paper focuses on exercises that serve as a useful complement to robotics curriculum and student robotics projects.

1. Introduction This paper describes a single semester computer vision course tailored to fourth year undergraduate students with strong engineering backgrounds and moderate computer programming skills. The students referred to in this paper are in the Weapons and Systems Engineering department at the United States Naval Academy. They have a strong mathematics background and a good foundation in Fourier transforms and frequency analysis. They have completed one MATLAB and one “C++” programming course, but have no computer vision or image processing background. Within the major, each student is required to complete a year long senior project. A large number of these projects are robotics-related. To support these projects, overviews of advanced topics, such as face recognition and computational intelligence, are included. We’ve found these topics also serve to foster long-term interest in the area of computer vision. To support the unique mission of the U. S. Naval Academy, this curriculum favors object identification topics. Each pattern recognition approach is compared and contrasted to the target recognition technologies currently used within the military. The state-of-the-art is discussed to give the student an understanding of capabilities and limitations of the technologies they may encounter during their military careers. We use Computer Vision by Shapiro and Stockman as a text [1].

2. Background A comprehensive survey of computer vision education has been compiled by Bebis et al in [2]. Bebis correctly points out that the computer vision field has grown rapidly in the past decade, and yet it is not well represented the curriculum most institutions. For over a decade, computer vision has been a part of the robotics curriculum in the Systems Engineering Department at the Naval Academy [3]. The course presented in this article serves as a stand-alone, yet complementary course to the robotic offerings within our department. In a survey of computer vision courses taken by Maxwell [4] five categories were identified: classic image processing, classic computer vision, application oriented,

Broussard, R., & Piepmeier, J. (2004, June), Undergraduate Computer Vision Curriculum To Complement A Robotics Program Paper presented at 2004 Annual Conference, Salt Lake City, Utah.

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2004 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015