Fast hand posture classification using depth features extracted from random line segments-Reference-Cited by-同舟云学术

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Fast hand posture classification using depth features extracted from random line segments

Published:2017-05 Issue: Volume:65 Page:1-10
ISSN:0031-3203
Container-title:Pattern Recognition
language:en
Short-container-title:Pattern Recognition

Author:

Nai Weizhi,Liu Yue,Rempel David,Wang Yongtian

Funder

National Natural Science Foundation of China

National Key Technology R&D Program of China

International Graduate Exchange Program of Beijing Institute of Technology

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference21 articles.

1. A robust static hand gesture recognition system using geometry based normalizations and krawtchouk moments;Priyal;Pattern Recognit.,2013

2. Hand shape recognition based on coherent distance shape contexts;Hu;Pattern Recognit.,2012

3. Robust part-based hand gesture recognition using kinect sensor;Ren;IEEE Trans. Multimed.,2013

4. K. Hu, L. Yinuthor, Multi-scale topological features for hand posture representation and analysis, in: ICCV, 2013.

5. N. Pugeault, R. Bowden, Spelling it out: Realtime asl fingerspelling recognition, in: ICCV Workshop, 2011.

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