Model-based 3D hand posture estimation from a single 2D image

By C. Chua, Haiying Guan, Yeong-Khing Ho. 2002

110 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Human-Computer Interaction

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Tae-Kyun Kim
2. Junsong Yuan
3. Ling Shao
4. Luc Van Gool
5. Li Liu
6. Dacheng Tao
7. Alan L. Yuille
8. Jungong Han

In Control and Systems Engineering

1. Tae-Kyun Kim
2. Junsong Yuan
3. Danhang Tang
4. Ali Erol
5. Ling Shao
6. Mircea Nicolescu
7. Zhou Ren
8. George Bebis

In Human-Computer Interaction

1. Tae-Kyun Kim
2. Junsong Yuan
3. Danhang Tang
4. Zhengyou Zhang
5. Zhou Ren
6. George Bebis
7. Tsz-Ho Yu
8. Ling Shao

Citations in the corpus

• RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, background): “1 Introduction In the past decades, there has been abundant computer vision research based on RGB images [3, 18, 90].”
• Robust Part-Based Hand Gesture Recognition Using Kinect Sensor (2013, methodology): “On one hand, to infer the pose of the palm and angles of the joints, many methods use colored markers to extract high-level features, such as the fi ngertip, joint locations or some anchor points on the palm [21]–[24].”
• Robust hand gesture recognition with kinect sensor (2011, unknown): “Existing vision-based approaches [1, 5, 6] are greatly”
• Robust hand gesture recognition based on finger-earth mover's distance with a commodity depth camera (2011, background): “Categories and Subject Descriptors H.1.2 [User/Machine Systems]: Human information processing; I.4.8 [Scene Analysis]: Depth cues, Shape General Terms Algorithm Keywords Hand Gesture Recognition, Human-Computer Interaction, Kinect Sensor, Finger-Earth Mover’s Distance”
• Vision-based hand pose estimation: A review (2007, background): “Chua 02 [112] 6(6) 1 Marker SF Inverse kinematics”
• Real-Time Articulated Hand Pose Estimation Using Semi-supervised Transductive Regression Forests (2013, methodology): “Hand pose estimation Earlier approaches for articulated hand pose estimation are diversified, such as coloured markers [6], probabilistic line matching [1], multi-camera network [13] and Bayesian filter with Chamfer matching [25].”