JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Depth Image based Chinese Learning Machine System Using Adjusted Chain Code
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Depth Image based Chinese Learning Machine System Using Adjusted Chain Code
Kim, Kisang; Choi, Hyung-Il;
  PDF(new window)
 Abstract
In this paper, we propose online Chinese character learning machine with a depth camera, where a system presents a Chinese character on a screen and a user is supposed to draw the presented Chinese character by his or her hand gesture. We develop the hand tracking method and suggest the adjusted chain code to represent constituent strokes of a Chinese character. For hand tracking, a fingertip is detected and verified. The adjusted chain code is designed to contain the information on order and relative length of each constituent stroke as well as the information on the directional variation of sample points. Such information is very efficient for a real-time match process and checking incorrectly drawn parts of a stroke.
 Keywords
Gesture Recognition;Chain Code;Learning Machine System;
 Language
Korean
 Cited by
 References
1.
Robert J. Elliott, Lakhdar Aggoun, and John B. Moore, Hidden Markov Models, Springer, 1995.

2.
M. Muller, "Dynamic time warping," Information retrieval for music and motion, pp.69-84, 2007.

3.
T. Starner and Alex Pentland, "Real-time american sign language recognition from video using hidden markov models," Motion-Based Recognition, Springer Netherlands, pp.227-243, 1997.

4.
A. Corradini, "Dynamic time warping for off-line recognition of a small gesture vocabulary," Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, 2001, Proceedings. IEEE ICCV Workshop on. IEEE, 2001.

5.
G. A. Ten Holt, M. J. T. Reinders, and E. A. Hendriks, "Multi-dimensional dynamic time warping for gesture recognition," Thirteenth annual conference of the Advanced School for Computing and Imaging, Vol.119, 2007.

6.
J. O. Wobbrock, A. D. Wilson, and Y. Li, "Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes," Proceedings of the 20th annual ACM symposium on User interface software and technology, ACM, 2007.

7.
L. Anthony and J. O. Wobbrock, "$N-Protractor: A fast and accurate multistroke recognizer," Proceedings of Graphics Interface (GI '12), Toronto, Ontario, Toronto, Ontario: Canadian Information Processing Society, pp.117-120, 2012.

8.
Vatavu, Radu-Daniel, Lisa Anthony, and Jacob O. Wobbrock, "Gestures as point clouds: a $ P recognizer for user interface prototypes," Proceedings of the 14th ACM international conference on Multimodal interaction, ACM, 2012.

9.
Y. Li, Protractor: A fast and accurate gesture recognizer, Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI '10). Atlanta, Georgia (April 10-15, 2010), New York: ACM Press, pp.2169-2172, 2010.

10.
L. Anthony and J. O. Wobbrock, A lightweight multistroke recognizer for user interface prototypes, Proceedings of Graphics Interface (GI '10), Ottawa, Ontario (May 31-June 2, 2010), Toronto, Ontario: Canadian Information Processing Society, pp.245-252, 2010.

11.
Mitra, Sushmita and Tinku Acharya, "Gesture recognition: A survey," Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 37.3, pp.311-324, 2007. crossref(new window)

12.
Y. Wu and T. S. Huang, "Vision-based gesture recognition: A review," Gesture-based communication in human-computer interaction, Springer Berlin Heidelberg, pp.103-115, 1999.

13.
H. K. Lee and J. H. Kim, "An HMM-based threshold model approach for gesture recognition," Pattern Analysis and Machine Intelligence, IEEE Transactions on 21.10. pp.961-973, 1999. crossref(new window)