CLC number: TP391; TP751
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2015-12-09
Cited: 0
Clicked: 7010
Shao-fan Wang, Chun Li, De-hui Kong, Bao-cai Yin. Extracting hand articulations from monocular depth images using curvature scale space descriptors[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(1): 41-54.
@article{title="Extracting hand articulations from monocular depth images using curvature scale space descriptors",
author="Shao-fan Wang, Chun Li, De-hui Kong, Bao-cai Yin",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="17",
number="1",
pages="41-54",
year="2016",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1500126"
}
%0 Journal Article
%T Extracting hand articulations from monocular depth images using curvature scale space descriptors
%A Shao-fan Wang
%A Chun Li
%A De-hui Kong
%A Bao-cai Yin
%J Frontiers of Information Technology & Electronic Engineering
%V 17
%N 1
%P 41-54
%@ 2095-9184
%D 2016
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1500126
TY - JOUR
T1 - Extracting hand articulations from monocular depth images using curvature scale space descriptors
A1 - Shao-fan Wang
A1 - Chun Li
A1 - De-hui Kong
A1 - Bao-cai Yin
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 17
IS - 1
SP - 41
EP - 54
%@ 2095-9184
Y1 - 2016
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1500126
Abstract: We propose a framework of hand articulation detection from a monocular depth image using curvature scale space (CSS) descriptors. We extract the hand contour from an input depth image, and obtain the fingertips and finger-valleys of the contour using the local extrema of a modified CSS map of the contour. Then we recover the undetected fingertips according to the local change of depths of points in the interior of the contour. Compared with traditional appearance-based approaches using either angle detectors or convex hull detectors, the modified CSS descriptor extracts the fingertips and finger-valleys more precisely since it is more robust to noisy or corrupted data; moreover, the local extrema of depths recover the fingertips of bending fingers well while traditional appearance-based approaches hardly work without matching models of hands. Experimental results show that our method captures the hand articulations more precisely compared with three state-of-the-art appearance-based approaches.
This paper proposed a framework of hand articulation detection from a monocular depth image using the curvature scale space (CSS) descriptors. The authors extract the hand contour from an input depth image, and obtain the fingertips and finger-valleys of the contour using the local extrema of a modified CSS map of the contour. This is the main contribution their work offers. They also recover undetected fingertips according to the local change of depths of points in the interior of the contour. Compared with traditional appearance-based approaches using either angle detectors or convex hull detectors, the modified CSS descriptor extracts the fingertips and finger-valleys more precisely since it is more robust to noisy or corrupted data; moreover, the local extrema of depths recover the fingertips of bending fingers well while traditional appearance-based approaches hardly work without matching models of hands. Totally, this paper uses a practical method to solve the hand articulation detection problem using depth data only.
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