Asee peer logo

An Introduction to Computer Vision for First-Year Electrical and Computer Engineering Students

Download Paper |

Conference

2019 FYEE Conference

Location

Penn State University , Pennsylvania

Publication Date

July 28, 2019

Start Date

July 28, 2019

End Date

July 30, 2019

Conference Session

M1A: WIP - Learning experiences 1

Tagged Topic

FYEE Conference - Paper Submission

Page Count

4

Permanent URL

https://peer.asee.org/33676

Download Count

11

Request a correction

Paper Authors

biography

Daniel Tai Klawson University of Maryland, College Park

visit author page

Daniel Klawson is a senior studying electrical engineering at the University of Maryland, College Park. He has been a teaching assistant for ENEE101 for the past four semesters.

visit author page

biography

Nathaniel Alexander Ferlic University of Maryland

visit author page

Current graduate student at the University of Maryland who's current teaching assistant position is for the course ENEE101.

visit author page

biography

Cheng Peng University of Maryland, College Park

visit author page

Advised by Prof. Rama Chellappa

visit author page

Download Paper |

Abstract

This work-in-progress paper will detail one of ENEE101’s newest modules, computer vision. ENEE101 is the introductory course to electrical and computer engineering (ECE) at the University of Maryland (UMD). This course provides first-year students with a glimpse into the broad field of ECE through high-level hands-on labs, with the goal of increasing student retention rates and boosting performance in sophomore-year courses; preliminary results have shown an upward trend in major retention and a downward trend in failures. Faculty-proposed modules cover a wide range of sub-disciplines in ECE, including optical communications, internet of things, and computer vision. Computer vision has become a popular topic in academia and industry due to its applications in machine learning, artificial intelligence, image recognition, self-driving cars, and more. Through our computer vision module for ENEE101, we seek to answer the following question: how can freshmen students, with almost no prior knowledge of even basic programming, actively learn and engage with computer vision? Our solution is to present students with three hands-on labs using the familiar Microsoft Kinect hardware along with open source computer vision software libraries. The labs we introduce cover depth sensing, hand tracking, facial recognition, and body detection. Each topic covers a single day of lab where the students are taught the basics of each concept and complete a C++ template with simple but elegant solutions, built and executed with Microsoft Visual Studio. The goal is to expose students to complex computer vision topics through easily understandable, real-life scenarios to help students realize the impactful applications of computer vision. By achieving this goal, we better prepare students for lives as scientists and engineers.

Klawson, D. T., & Ferlic, N. A., & Peng, C. (2019, July), An Introduction to Computer Vision for First-Year Electrical and Computer Engineering Students Paper presented at 2019 FYEE Conference , Penn State University , Pennsylvania. https://peer.asee.org/33676

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: © 2019 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