Outputs (16)

SAM-Net: semantic probabilistic and attention mechanisms of dynamic objects for self-supervised depth and camera pose estimation in visual odometry applications. (2021)
Journal Article
YANG, B., XU, X., REN, J., CHENG, L. GUO, L. and ZHANG, Z. 2022. SAM-Net: semantic probabilistic and attention mechanisms of dynamic objects for self-supervised depth and camera pose estimation in visual odometry applications. Pattern recognition letters [online], 153, pages 126-135. Available from: https://doi.org/10.1016/j.patrec.2021.11.028

3D scene understanding is an essential research topic in the field of Visual Odometry (VO). VO is usually built under the assumption of a static environment, which does not always hold in real scenarios. Existing works fail to consider the dynamic ob... Read More about SAM-Net: semantic probabilistic and attention mechanisms of dynamic objects for self-supervised depth and camera pose estimation in visual odometry applications..

Multi-segment majority voting decision fusion for MI EEG brain-computer interfacing. (2021)
Journal Article
PADFIELD, N., REN, J., QING, C., MURRAY, P., ZHAO, H. and ZHENG, J. 2021. Multi-segment majority voting decision fusion for MI EEG brain-computer interfacing. Cognitive computation [online], 13(6), pages 1484-1495. Available from: https://doi.org/10.1007/s12559-021-09953-3

Brain-computer interfaces (BCIs) based on the electroencephalogram (EEG) generated during motor imagery (MI) have the potential to be used in brain-controlled prosthetics, neurorehabilitation and gaming. Many MI EEG classification systems segment EEG... Read More about Multi-segment majority voting decision fusion for MI EEG brain-computer interfacing..

PCA-domain fused singular spectral analysis for fast and noise-robust spectral-spatial feature mining in hyperspectral classification. (2021)
Journal Article
YAN, Y., REN, J., LIU, Q., ZHAO, H., SUN, H. and ZABALZA, J. 2023. PCA-domain fused singular spectral analysis for fast and noise-robust spectral-spatial feature mining in hyperspectral classification. IEEE geoscience and remote sensing letters [online], 20, article 5505405. Available from: https://doi.org/10.1109/LGRS.2021.3121565

The principal component analysis (PCA) and 2-D singular spectral analysis (2DSSA) are widely used for spectral domain and spatial domain feature extraction in hyperspectral images (HSI). However, PCA itself suffers from low efficacy if no spatial inf... Read More about PCA-domain fused singular spectral analysis for fast and noise-robust spectral-spatial feature mining in hyperspectral classification..

Detection of the deep-sea plankton community in marine ecosystem with underwater robotic platform. (2021)
Journal Article
WANG, J., YANG, M., DING, Z., ZHENG, Q., WANG, D., KPALMA, K. and REN, J. 2021. Detection of the deep-sea plankton community in marine ecosystem with underwater robotic platform. Sensors [online], 21(20), article 6720. Available from: https://doi.org/10.3390/s21206720

Variations in the quantity of plankton impact the entire marine ecosystem. It is of great significance to accurately assess the dynamic evolution of the plankton for monitoring the marine environment and global climate change. In this paper, a novel... Read More about Detection of the deep-sea plankton community in marine ecosystem with underwater robotic platform..

MTFFNet: a multi-task feature fusion framework for Chinese painting classification. (2021)
Journal Article
JIANG, W., WANG, X., REN, J., LI, S., SUN, M., WANG, Z. and JIN, J.S. 2021. MTFFNet: a multi-task feature fusion framework for Chinese painting classification. Cognitive computation [online], 13(5), pages 1287-1296. Available from: https://doi.org/10.1007/s12559-021-09896-9

Different artists have their unique painting styles, which can be hardly recognized by ordinary people without professional knowledge. How to intelligently analyze such artistic styles via underlying features remains to be a challenging research prob... Read More about MTFFNet: a multi-task feature fusion framework for Chinese painting classification..

SpaSSA: superpixelwise adaptive SSA for unsupervised spatial-spectral feature extraction in hyperspectral image. (2021)
Journal Article
SUN, G., FU, H., REN, J., ZHANG, A., ZABALZA, J., JIA, X. and ZHAO, H. 2022. SpaSSA: superpixelwise adaptive SSA for unsupervised spatial-spectral feature extraction in hyperspectral image. IEEE transactions on cybernetics [online], 52(7), pages 6158-6169. Available from: https://doi.org/10.1109/TCYB.2021.3104100

Singular spectral analysis (SSA) has recently been successfully applied to feature extraction in hyperspectral image (HSI), including conventional (1-D) SSA in spectral domain and 2-D SSA in spatial domain. However, there are some drawbacks, such as... Read More about SpaSSA: superpixelwise adaptive SSA for unsupervised spatial-spectral feature extraction in hyperspectral image..

Sparse learning of band power features with genetic channel selection for effective classification of EEG signals. (2021)
Journal Article
PADFIELD, N., REN, J., MURRAY, P. and ZHAO, H. 2021. Sparse learning of band power features with genetic channel selection for effective classification of EEG signals. Neurocomputing [online], 463, pages 566-579. Available from: https://doi.org/10.1016/j.neucom.2021.08.067

In this paper, we present a genetic algorithm (GA) based band power feature sparse learning (SL) approach for classification of electroencephalogram (EEG) (GABSLEEG) in motor imagery (MI) based brain-computer interfacing (BCI). The band power in the... Read More about Sparse learning of band power features with genetic channel selection for effective classification of EEG signals..

Spectral-spatial self-attention networks for hyperspectral image classification. (2021)
Journal Article
ZHANG, X., SUN, G., JIA, X., WU, L., ZHANG, A., REN, J., FU, H. and YAO, Y. 2022. Spectral-spatial self-attention networks for hyperspectral image classification. IEEE transactions on geoscience and remote sensing [online], 60, article 5512115. Available from: https://doi.org/10.1109/TGRS.2021.3102143

This study presents a spectral-spatial self-attention network (SSSAN) for classification of hyperspectral images (HSIs), which can adaptively integrate local features with long-range dependencies related to the pixel to be classified. Specifically, i... Read More about Spectral-spatial self-attention networks for hyperspectral image classification..

A novel multi-stage residual feature fusion network for detection of COVID-19 in chest X-ray images. (2021)
Journal Article
FANG, Z., REN, J., MACLELLAN, C., LI, H., ZHOA, H., HUSSAIN, A. and FORTINO, G. 2022. A novel multi-stage residual feature fusion network for detection of COVID-19 in chest X-ray images. IEEE transactions on molecular, biological and multi-scale communications [online], 8(1), pages 17-27. Available from: https://doi.org/10.1109/tmbmc.2021.3099367

To suppress the spread of COVID-19, accurate diagnosis at an early stage is crucial, chest screening with radiography imaging plays an important role in addition to the real-time reverse transcriptase polymerase chain reaction (RT-PCR) swab test. Due... Read More about A novel multi-stage residual feature fusion network for detection of COVID-19 in chest X-ray images..

一种基于模糊成像机理的QR码图像快速盲复原方法. (2021)
Journal Article
CHEN, R., ZHENG, Z., ZHAO, H., REN, J. and TAN, H. 2021. 一种基于模糊成像机理的QR 码图像快速 盲复原方法. = [Fast blind restoration of QR code images based on blurred imaging mechanism]. Guangzi xuebao/Acta photonica sinica [online], 50(7), article 0710003. Available from: https://doi.org/10.3788/gzxb20215007.0710003

A fast blind restoration method of QR code images was proposed based on a blurred imaging mechanism. On the basis of the research on the centroid invariance of the blurred imaging diffuse light spots, the circular finder pattern is designed. When the... Read More about 一种基于模糊成像机理的QR码图像快速盲复原方法..