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http://dx.doi.org/10.5762/KAIS.2019.20.9.525

Robust Hand Region Extraction Using a Joint-based Model  

Jang, Seok-Woo (Department of Software, Anyang University)
Kim, Sul-Ho (School of Software, Soongsil University)
Kim, Gye-Young (School of Software, Soongsil University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.20, no.9, 2019 , pp. 525-531 More about this Journal
Abstract
Efforts to utilize human gestures to effectively implement a more natural and interactive interface between humans and computers have been ongoing in recent years. In this paper, we propose a new algorithm that accepts consecutive three-dimensional (3D) depth images, defines a hand model, and robustly extracts the human hand region based on six palm joints and 15 finger joints. Then, the 3D depth images are adaptively binarized to exclude non-interest areas, such as the background, and accurately extracts only the hand of the person, which is the area of interest. Experimental results show that the presented algorithm detects only the human hand region 2.4% more accurately than the existing method. The hand region extraction algorithm proposed in this paper is expected to be useful in various practical applications related to computer vision and image processing, such as gesture recognition, virtual reality implementation, 3D motion games, and sign recognition.
Keywords
Hand Region; Model; Point Cloud; Binarization; Depth Image; Preprocessing;
Citations & Related Records
Times Cited By KSCI : 8  (Citation Analysis)
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