The Experts below are selected from a list of 29394 Experts worldwide ranked by ideXlab platform
Lu Yang - One of the best experts on this subject based on the ideXlab platform.
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survey on 3d hand Gesture Recognition
IEEE Transactions on Circuits and Systems for Video Technology, 2016Co-Authors: Hong Cheng, Lu YangAbstract:Three-dimensional hand Gesture Recognition has attracted increasing research interests in computer vision, pattern Recognition, and human-computer interaction. The emerging depth sensors greatly inspired various hand Gesture Recognition approaches and applications, which were severely limited in the 2D domain with conventional cameras. This paper presents a survey of some recent works on hand Gesture Recognition using 3D depth sensors. We first review the commercial depth sensors and public data sets that are widely used in this field. Then, we review the state-of-the-art research for 3D hand Gesture Recognition in four aspects: 1) 3D hand modeling; 2) static hand Gesture Recognition; 3) hand trajectory Gesture Recognition; and 4) continuous hand Gesture Recognition. While the emphasis is on 3D hand Gesture Recognition approaches, the related applications and typical systems are also briefly summarized for practitioners.
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Survey on 3D Hand Gesture Recognition
IEEE Transactions on Circuits and Systems for Video Technology, 2016Co-Authors: Hong Cheng-yu, Lu Yang, Zicheng LiuAbstract:3D hand Gesture Recognition has attracted increasing research interests in computer vision, pattern Recognition and human-computer interaction. The emerging depth sensors greatly inspired various hand Gesture Recognition approaches and applications, which were severely limited in the 2D domain with conventional cameras. This paper presents a survey of some recent works on hand Gesture Recognition using 3D depth sensors. We first review the commercial depth sensors and public datasets which are widely used in this field. Then, we review the state-of-the-art research for 3D hand Gesture Recognition in four aspects: 3D hand modeling, static hand Gesture Recognition, hand trajectory Gesture Recognition and continuous hand Gesture Recognition.While the emphasis is on 3D hand Gesture Recognition approaches, the related applications and typical systems are also briefly summarized for practitioners.
Zicheng Liu - One of the best experts on this subject based on the ideXlab platform.
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Survey on 3D Hand Gesture Recognition
IEEE Transactions on Circuits and Systems for Video Technology, 2016Co-Authors: Hong Cheng-yu, Lu Yang, Zicheng LiuAbstract:3D hand Gesture Recognition has attracted increasing research interests in computer vision, pattern Recognition and human-computer interaction. The emerging depth sensors greatly inspired various hand Gesture Recognition approaches and applications, which were severely limited in the 2D domain with conventional cameras. This paper presents a survey of some recent works on hand Gesture Recognition using 3D depth sensors. We first review the commercial depth sensors and public datasets which are widely used in this field. Then, we review the state-of-the-art research for 3D hand Gesture Recognition in four aspects: 3D hand modeling, static hand Gesture Recognition, hand trajectory Gesture Recognition and continuous hand Gesture Recognition.While the emphasis is on 3D hand Gesture Recognition approaches, the related applications and typical systems are also briefly summarized for practitioners.
Hong Cheng-yu - One of the best experts on this subject based on the ideXlab platform.
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Survey on 3D Hand Gesture Recognition
IEEE Transactions on Circuits and Systems for Video Technology, 2016Co-Authors: Hong Cheng-yu, Lu Yang, Zicheng LiuAbstract:3D hand Gesture Recognition has attracted increasing research interests in computer vision, pattern Recognition and human-computer interaction. The emerging depth sensors greatly inspired various hand Gesture Recognition approaches and applications, which were severely limited in the 2D domain with conventional cameras. This paper presents a survey of some recent works on hand Gesture Recognition using 3D depth sensors. We first review the commercial depth sensors and public datasets which are widely used in this field. Then, we review the state-of-the-art research for 3D hand Gesture Recognition in four aspects: 3D hand modeling, static hand Gesture Recognition, hand trajectory Gesture Recognition and continuous hand Gesture Recognition.While the emphasis is on 3D hand Gesture Recognition approaches, the related applications and typical systems are also briefly summarized for practitioners.
Joseph A. Paradiso - One of the best experts on this subject based on the ideXlab platform.
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The Gesture Recognition Toolkit
Journal of Machine Learning Research, 2014Co-Authors: Nicholas Gillian, Joseph A. ParadisoAbstract:The Gesture Recognition Toolkit is a cross-platform open-source C++ library designed to make real-time machine learning and Gesture Recognition more accessible for non-specialists. Emphasis is placed on ease of use, with a consistent, minimalist design that promotes accessibility while supporting flexibility and customization for advanced users. The toolkit features a broad range of classification and regression algorithms and has extensive support for building real-time systems. This includes algorithms for signal processing, feature extraction and automatic Gesture spotting.
Hong Cheng - One of the best experts on this subject based on the ideXlab platform.
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survey on 3d hand Gesture Recognition
IEEE Transactions on Circuits and Systems for Video Technology, 2016Co-Authors: Hong Cheng, Lu YangAbstract:Three-dimensional hand Gesture Recognition has attracted increasing research interests in computer vision, pattern Recognition, and human-computer interaction. The emerging depth sensors greatly inspired various hand Gesture Recognition approaches and applications, which were severely limited in the 2D domain with conventional cameras. This paper presents a survey of some recent works on hand Gesture Recognition using 3D depth sensors. We first review the commercial depth sensors and public data sets that are widely used in this field. Then, we review the state-of-the-art research for 3D hand Gesture Recognition in four aspects: 1) 3D hand modeling; 2) static hand Gesture Recognition; 3) hand trajectory Gesture Recognition; and 4) continuous hand Gesture Recognition. While the emphasis is on 3D hand Gesture Recognition approaches, the related applications and typical systems are also briefly summarized for practitioners.
