Understanding of hand motion in sentence level using HMM

Boohyung Lee; Youngjoon Han; Hwanik Chung; Hernsoo Hahn

doi:10.1117/12.526425

7 June 2004 Understanding of hand motion in sentence level using HMM

Boohyung Lee, Youngjoon Han, Hwanik Chung, Hernsoo Hahn

Proceedings Volume 5292, Human Vision and Electronic Imaging IX; (2004) https://doi.org/10.1117/12.526425
Event: Electronic Imaging 2004, 2004, San Jose, California, United States

Abstract

This paper proposes a new method that can recognize a sequence of hand motion expressing a sentence in sign language. Recognition procedure is divided into two steps: separation of the sequence of hand motions into the sub-sequences each of which expresses one word and combination of the words in order to construct a sentence having a meaning. In the first step, sequences of hand motion images are segmented by testing the continuity of the hand motions and by the multiscale image segmentation scheme. The trajectory of the hand motions are estimated by the affine transformation. Each sign in the sentence is represented by the extended chereme analysis model and each chereme is represented by the status vector for determining the transition in the HMM. In the second step, each sentence is also represented by the HMM. The Viterbi algorithm and context-dependent HMM are used to find the best state sequence in the HMM. The proposed algorithm has been tested with ten sequences of images, each of which expresses a sentence in Korean sign language. The experimental results have shown that the proposed algorithm can separate the sentence level image sequence into the word level sub-sequences with the success rate of 75% on average and recognize the sentence with the success rate of 80%.

Citation Download Citation

Boohyung Lee, Youngjoon Han, Hwanik Chung, and Hernsoo Hahn "Understanding of hand motion in sentence level using HMM", Proc. SPIE 5292, Human Vision and Electronic Imaging IX, (7 June 2004); https://doi.org/10.1117/12.526425

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available