J
Juan Cheng
Researcher at Hefei University of Technology
Publications - 49
Citations - 2096
Juan Cheng is an academic researcher from Hefei University of Technology. The author has contributed to research in topics: Image fusion & Computer science. The author has an hindex of 15, co-authored 46 publications receiving 950 citations. Previous affiliations of Juan Cheng include University of Science and Technology of China.
Papers
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Journal ArticleDOI
Infrared and visible image fusion with convolutional neural networks
TL;DR: This paper proposes an infrared fusion image that combines infrared and visible images of the same scene to generate a composite image which can provide a more comprehensive description of the scene.
Proceedings ArticleDOI
A medical image fusion method based on convolutional neural networks
TL;DR: Experimental results demonstrate that the proposed convolutional neural networks method can achieve promising results in terms of both visual quality and objective assessment.
Journal ArticleDOI
A Framework for Daily Activity Monitoring and Fall Detection Based on Surface Electromyography and Accelerometer Signals
TL;DR: A framework for activity awareness using surface electromyography and accelerometer signals is proposed and a continuous daily activity monitoring and fall detection scheme was performed, demonstrating the excellent fall detection performance and the great feasibility of the proposed method in daily activities awareness.
Journal ArticleDOI
EEG-based Emotion Recognition via Channel-wise Attention and Self Attention
TL;DR: In this paper, an attention-based convolutional recurrent neural network (ACRNN) was proposed to extract more discriminative features from EEG signals and improve the accuracy of emotion recognition.
Journal ArticleDOI
Multi-focus image fusion: A Survey of the state of the art
TL;DR: A comprehensive overview of existing multi-focus image fusion methods is presented and a new taxonomy is introduced to classify existing methods into four main categories: transformdomain methods, spatial domain methods, methods combining transform domain and spatial domain, and deep learning methods.