Extracting hand articulations from monocular depth images using curvature scale space descriptors

作     者：Shao-fan WANG Chun LI De-hui KONG Bao-cai YIN 

作者机构：Beijing Key Laboratory of Multimedia and Intelligent Software TechnologyCollege of Metropolitan Transportation Beijing University of Technology Beijing 100124 China School of Software Technology Dalian University of Technology Dalian 116024 China Collaborative Innovation Center of Electric Vehicles in Beijing Beijing 100081 China 

出 版 物：《Frontiers of Information Technology & Electronic Engineering》 (信息与电子工程前沿（英文版）)

年 卷 期：2016年第17卷第1期

页      面：41-54页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported by the National Natural Science Foundation of China(Nos.61227004 61370120 61390510 61300065 and 61402024) Beijing Municipal Natural Science Foundation,China(No.4142010) Beijing Municipal Commission of Education,China(No.km201410005013) the Funding Project for Academic Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality,China 

主　　题：Curvature scale space (CSS), Hand articulation, Convex hull, Hand contour 

摘      要：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.