手势识别

基于超像/体素图的手势识别方法
本项目主要围绕手势数据预处理，静、动态手势特征提取和距离度量，实时手势交互应用这三个方面展开研究。
基于超像/体素图的手势识别方法
基于长短时域特征的动态手势识别方法
在时域引入注意力机制，利用三维卷积网络和时间卷积网络分别提取短时和长时动态手势特征，进而构建高精度的动态手势识别算法。
基于长短时域特征的动态手势识别方法
基于超像素图EMD距离的手势识别及交互
本研究在结合了超像素分割、手势结构和EMD距离的基础上，提出了一种新的手势表示形式和新的距离度量标准。
基于超像素图EMD距离的手势识别及交互
Short-Term Temporal Convolutional Networks for Dynamic Hand Gesture Recognition
In this paper, we present a multimodal gesture recognition method based on 3D densely convolutional networks (3D-DenseNets) and improved temporal convolutional networks (TCNs).
Short-Term Temporal Convolutional Networks for Dynamic Hand Gesture Recognition
A hand gesture recognition system based on canonical superpixel-graph
This paper presents a new hand gesture recognition system based on a novel canonical superpixel-graph earth mover’s distance (CSG-EMD) metric.
A hand gesture recognition system based on canonical superpixel-graph
Superpixel-Based Hand Gesture Recognition With Kinect Depth Camera
In this work, a novel distance metric, superpixel earth mover’s distance (SP-EMD), is proposed to measure the dissimilarity between the hand gestures.
Superpixel-Based Hand Gesture Recognition With Kinect Depth Camera