Professor Jinchang Ren's Outputs (154)

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..

Folded LDA: extending the linear discriminant analysis algorithm for feature extraction and data reduction in hyperspectral remote sensing. (2021)
Journal Article
FABIYI, S.D., MURRAY, P., ZABALZA, J. and REN, J. 2021. Folded LDA: extending the linear discriminant analysis algorithm for feature extraction and data reduction in hyperspectral remote sensing. IEEE journal of selected topics in applied earth observations and remote sensing [online], 14, pages 12312-12331. Available from: https://doi.org/10.1109/JSTARS.2021.3129818

The rich spectral information provided by hyperspectral imaging (HSI) has made this technology very useful in the classification of remotely sensed data. However, classification of hyperspectral data is typically affected by noise and the Hughes phen... Read More about Folded LDA: extending the linear discriminant analysis algorithm for feature extraction and data reduction in hyperspectral remote sensing..

Effective extraction of ventricles and myocardium objects from cardiac magnetic resonance images with a multi-task learning U-net. (2021)
Journal Article
REN, J., SUN, H., ZHAO, H., GAO, H., MACLELLAN, C., ZHAO, S. and LUO, X. 2022. Effective extraction of ventricles and myocardium objects from cardiac magnetic resonance images with a multi-task learning U-net. Pattern recognition letters [online], 155, pages 165-170. Available from: https://doi.org/10.1016/j.patrec.2021.10.025

Accurate extraction of semantic objects such as ventricles and myocardium from magnetic resonance (MR) images is one essential but very challenging task for the diagnosis of the cardiac diseases. To tackle this problem, in this paper, an automatic en... Read More about Effective extraction of ventricles and myocardium objects from cardiac magnetic resonance images with a multi-task learning U-net..

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..

A novel robust low-rank multi-view diversity optimization model with adaptive-weighting based manifold learning. (2021)
Journal Article
TAN, J., YANG, Z., REN, J., WANG, B., CHENG, Y. and LING, W.-K. 2021. A novel robust low-rank multi-view diversity optimization model with adaptive-weighting based manifold learning. Pattern recognition [online], 122, articles 108298. Available from: https://doi.org/10.1016/j.patcog.2021.108298

Multi-view clustering has become a hot yet challenging topic, due mainly to the independence of and information complementarity between different views. Although good results are achieved to a certain extent from typical methods including multi-view... Read More about A novel robust low-rank multi-view diversity optimization model with adaptive-weighting based manifold learning..

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..

Fast blind deblurring of QR code images based on adaptive scale control. (2021)
Journal Article
CHEN, R., ZHENG, Z., PAN, J., YU, Y., ZHAO, H. and REN, J. 2022. Fast blind deblurring of QR code images based on adaptive scale control. Mobile networks and applications [online], 26(6), pages 2472-2487. Available from: https://doi.org/10.1007/s11036-021-01780-y

With the development of 5G technology, the short delay requirements of commercialization and large amounts of data change our lifestyle day-to-day. In this background, this paper proposes a fast blind deblurring algorithm for QR code images, which ma... Read More about Fast blind deblurring of QR code images based on adaptive scale control..

Fast restoration for out-of-focus blurred images of QR code with edge prior information via image sensing. (2021)
Journal Article
CHEN, R., ZHENG, Z., YU, Y., ZHAO, H., REN, J. and TAN, H.-Z. 2021. Fast restoration for out-of-focus blurred images of QR code with edge prior information via image sensing. IEEE sensors journal [online], 21(16), article 103048, pages 18222-18236. Available from: https://doi.org/10.1109/JSEN.2021.3085568

Out-of-focus blurring of the QR code is very common in mobile Internet systems, which often causes failure of authentication as a result of a misreading of the information hence adversely affects the operation of the system. To tackle this difficulty... Read More about Fast restoration for out-of-focus blurred images of QR code with edge prior information via image sensing..

Novel gumbel-softmax trick enabled concrete autoencoder with entropy constraints for unsupervised hyperspectral band selection. (2021)
Journal Article
SUN, H., REN, J., ZHAO, H., YUEN, P. and TSCHANNERL, J. 2022. Novel gumbel-softmax trick enabled concrete autoencoder with entropy constraints for unsupervised hyperspectral band selection. IEEE transactions on geoscience and remote sensing [online], 60, article 5506413. Available from: https://doi.org/10.1109/TGRS.2021.3075663

As an important topic in hyperspectral image (HSI) analysis, band selection has attracted increasing attention in the last two decades for dimensionality reduction in HSI. With the great success of deep learning (DL)-based models recently, a robust u... Read More about Novel gumbel-softmax trick enabled concrete autoencoder with entropy constraints for unsupervised hyperspectral band selection..

Nondestructive phenolic compounds measurement and origin discrimination of peated barley malt using near-infrared hyperspectral imagery and machine learning. (2021)
Journal Article
YAN, Y., REN, J., TSCHANNERL, J., ZHAO, H., HARRISON, B. and JACK, F. 2021. Nondestructive phenolic compounds measurement and origin discrimination of peated barley malt using near-infrared hyperspectral imagery and machine learning. IEEE transactions on instrumentation and measurement [online], 70, article 5010715. Available from: https://doi.org/10.1109/TIM.2021.3082274

Quantifying phenolic compound in peated barley malt and discriminating its origin are essential to maintain the aroma of high-quality Scottish whisky during the manufacturing process. The content of the total phenol varies in peated barley malts, whi... Read More about Nondestructive phenolic compounds measurement and origin discrimination of peated barley malt using near-infrared hyperspectral imagery and machine learning..

Fusion of infrared and visible images for remote detection of low-altitude slow-speed small targets. (2021)
Journal Article
SUN, H., LIU, Q., WANG, J., REN, J., WU, Y., ZHAO, H. and LI, H. 2021. Fusion of infrared and visible images for remote detection of low-altitude slow-speed small targets. IEEE journal of selected topics in applied earth observations and remote sensing [online], 14, pages 2971-2983. Available from: https://doi.org/10.1109/JSTARS.2021.3061496

Detection of the low-altitude and slow-speed small (LSS) targets is one of the most popular research topics in remote sensing. Despite of a few existing approaches, there is still an accuracy gap for satisfying the practical needs. As the LSS targets... Read More about Fusion of infrared and visible images for remote detection of low-altitude slow-speed small targets..

A new cost function for spatial image steganography based on 2D-SSA and WMF. (2021)
Journal Article
XIE, G., REN, J., MARSHALL, S., ZHAO, H. and LI, H. 2021. A new cost function for spatial image steganography based on 2D-SSA and WMF. IEEE access [online], 9, pages 30604-30614. Available from: https://doi.org/10.1109/ACCESS.2021.3059690

As an essential tool for secure communications, adaptive steganography aims to communicate secret information with the least security cost. Inspired by the Ranking Priority Profile (RPP), we propose a novel two-step cost function for adaptive stegano... Read More about A new cost function for spatial image steganography based on 2D-SSA and WMF..

A framework for breast cancer classification using multi-DCNNs. (2021)
Journal Article
RAGAB, D.A., ATTALLAH, O., SHARKAS, M., REN, J. and MARSHALL, S. 2021. A framework for breast cancer classification using multi-DCNNs. Computers in biology and medicine [online], 131, article number 104245. Available from: https://doi.org/10.1016/j.compbiomed.2021.104245

Deep learning (DL) is the fastest-growing field of machine learning (ML). Deep convolutional neural networks (DCNN) are currently the main tool used for image analysis and classification purposes. There are several DCNN architectures among them AlexN... Read More about A framework for breast cancer classification using multi-DCNNs..