.png){kind=icon}
返回
DL-Net: Sparsity Prior Learning for Grasp Pattern Recognition
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2023.3236402.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The purpose of grasp pattern recognition is to determine the grasp type for an object to be grasped, which can be applied to prosthetic hand control and ease the burden of amputees. To enhance the performance of grasp pattern recognition, we propose a network DL-Net inspired by dictionary learning. Our method includes two parts: 1) forward propagation for sparsity representation learning and 2) backward propagation for dictionary learning, which utilizes the sparsity prior effectively and learns a discriminative dictionary with stronger expressive ability from a mass of training data. The experiment was performed on two household object datasets, the RGB-D Object dataset, and the Hit-GPRec dataset. The experimental results illustrate that the DL-Net performs better than traditional deep learning methods in grasp pattern recognition.
Keyword:
Feature extraction
Pattern recognition
Dictionaries
Sparse matrices
Convolutional neural networks
Image reconstruction
Deep learning
Computer vision
Grasp pattern recognition
computer vision
dictionary learning
deep learning
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Investigation of Different Approaches to Real-Time Control of Prosthetic Hands With Electromyography Signals.png)
IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏A micromachined efficient parametric array loudspeaker with a wide radiation frequency band具有宽辐射频带的微机械高效参量阵列扬声器
PREAI
分享收藏Proximity Extension Assay in Combination with Next-Generation Sequencing for High-throughput Proteome-wide Analysis邻近延伸分析与下一代测序相结合，用于高通量蛋白质组范围的分析
OAAI
OAAI分享收藏
查看更多内容