Parsing the Hand in Depth Images

By Hui Liang, Junsong Yuan, Daniel Thalmann. 2014

99 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. Kyoung Mu Lee
2. Luc Van Gool
3. Gyeongsik Moon
4. Ming-Hsuan Yang
5. Jitendra Malik
6. Dacheng Tao
7. Xiaogang Wang
8. Xiaoou Tang

In Control and Systems Engineering

1. Gyeongsik Moon
2. Kyoung Mu Lee
3. Ju Yong Chang
4. Yichen Wei
5. Xiao Sun
6. Guillermo Garcia-Hernando
7. Seungryul Baek
8. Tae-Kyun Kim

In Human-Computer Interaction

1. Gyeongsik Moon
2. Kyoung Mu Lee
3. Ju Yong Chang
4. Yichen Wei
5. Tae-Kyun Kim
6. Xiao Sun
7. Shanxin Yuan
8. Sergio Escalera

Citations in the corpus

• Spatial Attention Deep Net with Partial PSO for Hierarchical Hybrid Hand Pose Estimation (2016, background): “Many prior works have achieved good performance by different methods [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16].”
• Recent methods and databases in vision-based hand gesture recognition: A review (2015, background): “[122] proposed a robust hand parsing scheme to extract a high-level description of the hand from the depth images.”
• First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations (2017, background): “3D hand pose estimation: Mainly due to the recent availability of RGB-D sensors, the field has made significant progress in object-less third-person view [22, 25, 28, 37, 39, 41, 45, 53, 60, 80] and more modest advances in first-person view [10, 35, 38, 48].”
• V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map (2017, background): “Random forest-based meth-ods [21, 23, 39, 41–43 , 48] provide fast and accurate performance performance.”
• Superpixel-Based Hand Gesture Recognition With Kinect Depth Camera (2015, methodology): “For efficient hand gesture recognition, a superpixel-based representation of the hand shape, inspired by the widely used concept of superpixel [15], [16], is introduced in this work, from which a new distance metric, Superpixel Earth Mover’s Distance (SP-EMD), is proposed to measure the dissimilarit”
• Cascaded hand pose regression (2015, methodology): “holistic regression Many methods [6, 39, 10, 22] estimate hand joints individually by following the per-pixel classification approaches for human body pose recognition [18, 29].”
• Challenges in multimodal gesture recognition (2016, methodology): “The work of (Liang et al., 2014) presents a depth-based method for hand detection and pose recognition by segmentation of different hand parts.”