Masters Theses

Cao Dong

Abstract

"American Sign Language (ASL) fingerspelling recognition using marker-less vision sensors is a challenging task due to the complexity of ASL signs, self-occlusion of the hand, and limited resolution of the sensors. This thesis describes a new method for ASL fingerspelling recognition using a low-cost vision camera, which is Microsoft's Kinect. A segmented hand configuration is first obtained by using a depth contrast feature based per-pixel classification algorithm. Then, a hierarchical mode-finding method is developed and implemented to localize hand joint positions under kinematic constraints. Finally, a Random Decision Forest (RDF) classifier is built to recognize ASL signs according to the joint angles. To validate the performance of this method, a dataset containing 75,000 samples of 24 static ASL alphabet signs is used. The system is able to achieve a mean accuracy of 92%. We have also used a publicly available dataset from Surrey University to evaluate our method. The results have shown that our method can achieve higher accuracy in recognizing ASL alphabet signs in comparison to the previous benchmarks."--Abstract, page iii.

Advisor(s)

Leu, M. C. (Ming-Chuan)

Committee Member(s)

Liou, Frank W.
Yin, Zhaozheng

Department(s)

Mechanical and Aerospace Engineering

Degree Name

M.S. in Mechanical Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Spring 2015

Pagination

vii, 39 pages

Note about bibliography

Includes bibliographical references (pages 36-38).

Rights

© 2015 Cao Dong, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Subject Headings

American Sign Language -- Computer simulationThree-dimensional imagingKinect (Programmable controller) -- Programming

Thesis Number

T 10670

Electronic OCLC #

913484996

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