A Framework for Articulated Hand Pose Estimation and Evaluation

Autor:	Horst Bischof, Matthias Rüther, David Ferstl, Gernot Riegler
Rok vydání:	2015
Předmět:	Training set business.industry Computer science Inference computer.software_genre Machine learning Synthetic data Random forest Test (assessment) Work (electrical) Segmentation Artificial intelligence Data mining business Pose computer
Zdroj:	Image Analysis ISBN: 9783319196640 SCIA
DOI:	10.1007/978-3-319-19665-7_4
Popis:	We present in this paper a framework for articulated hand pose estimation and evaluation. Within this framework we implemented recently published methods for hand segmentation and inference of hand postures. We further propose a new approach for the segmentation and extend existing convolutional network based inference methods. Additionally, we created a new dataset that consists of a synthetically generated training set and accurately annotated test sequences captured with two different consumer depth cameras. The evaluation shows that we can improve with our methods the state-of-the-art. To foster further research, we will make all sources and the complete dataset used in this work publicly available.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::317f521fb16c2a6865c0de1a9be9f7c5 https://doi.org/10.1007/978-3-319-19665-7_4 Zobrazit plný text záznamu