All Outputs (248)

Human pose estimation-based real-time gait analysis using convolutional neural network. (2020)
Journal Article
ROHAN, A., RABAH, M., HOSNY, T. and KIM, S.-H. 2020. Human pose estimation-based real-time gait analysis using convolutional neural network. IEEE access [online] 8, pages 191542-191550. Available from: https://doi.org/10.1109/ACCESS.2020.3030086

Gait analysis is widely used in clinical practice to help in understanding the gait abnormalities and its association with a certain underlying medical condition for better diagnosis and prognosis. Several technologies embedded in the specialized dev... Read More about Human pose estimation-based real-time gait analysis using convolutional neural network..

SAFDet: a semi-anchor-free detector for effective detection of oriented objects in aerial images. (2020)
Journal Article
FANG, Z., REN, J., SUN, H., MARSHALL, S., HAN, J. and ZHAO, H. 2020. SAFDet: a semi-anchor-free detector for effective detection of oriented objects in aerial images. Remote sensing [online], 12(19), article 3225. Available from: https://doi.org/10.3390/rs12193225

An oriented bounding box (OBB) is preferable over a horizontal bounding box (HBB) in accurate object detection. Most of existing works utilize a two-stage detector for locating the HBB and OBB, respectively, which have suffered from the misaligned ho... Read More about SAFDet: a semi-anchor-free detector for effective detection of oriented objects in aerial images..

A novel intelligent computational approach to model epidemiological trends and assess the impact of non-pharmacological interventions for COVID-19. (2020)
Journal Article
REN, J., YAN, Y., ZHAO, H., MA, P., ZABALZA, J., HUSSAIN, Z., LUO, S., DAI, Q., ZHAO, S., SHEIKH, A., HUSSAIN, A. and LI, H. 2020. A novel intelligent computational approach to model epidemiological trends and assess the impact of non-pharmacological interventions for COVID-19. IEEE Journal of biomedical and health informatics [online], 24(12), pages 3551-3563. Available from: https://doi.org/10.1109/jbhi.2020.3027987

The novel coronavirus disease 2019 (COVID-19) pandemic has led to a worldwide crisis in public health. It is crucial we understand the epidemiological trends and impact of non-pharmacological interventions (NPIs), such as lockdowns for effective mana... Read More about A novel intelligent computational approach to model epidemiological trends and assess the impact of non-pharmacological interventions for COVID-19..

Multi-sensor fusion-based time-frequency imaging and transfer learning for spherical tank crack diagnosis under variable pressure conditions. (2020)
Journal Article
HASAN, M.J., ISLAM, M.M.M. and KIM, J.-M. 2021. Multi-sensor fusion-based time-frequency imaging and transfer learning for spherical tank crack diagnosis under variable pressure conditions. Measurement [online], 168, article 108478. Available from: https://doi.org/10.1016/j.measurement.2020.108478

In this paper, a crack diagnosis framework is proposed that combines a new signal-to-imaging technique and transfer learning-aided deep learning framework to automate the diagnostic process. The objective of the signal-to-imaging technique is to conv... Read More about Multi-sensor fusion-based time-frequency imaging and transfer learning for spherical tank crack diagnosis under variable pressure conditions..

Rewritable and sustainable 2D barcode for traceability application in smart IoT based fault-tolerant mechanism. (2020)
Journal Article
CHEN, R., YU, Y., XIE, S., ZHAO, H., LIU, S., REN, J. and TAN, H.-Z. 2020. Rewritable and sustainable 2D barcode for traceability application in smart IoT based fault-tolerant mechanism. Sustainability [online], 12(17), article 7192. Available from: https://doi.org/10.3390/su12177192

With the development of the Internet of Things (IoT) technology, two-dimensional (2D) barcodes are widely used in smart IoT applications as a perception portal. In industries with many circulations and testing links like traceability, since the exist... Read More about Rewritable and sustainable 2D barcode for traceability application in smart IoT based fault-tolerant mechanism..

Helmet use detection of tracked motorcycles using CNN-based multi-task learning. (2020)
Journal Article
LIN, H., DENG, J.D., ALBERS, D. and SIEBERT, F.W. 2020. Helmet use detection of tracked motorcycles using CNN-based multi-task learning. IEEE access [online], 8, pages 162073-162084. Available from: https://doi.org/10.1109/access.2020.3021357

Automated detection of motorcycle helmet use through video surveillance can facilitate efficient education and enforcement campaigns that increase road safety. However, existing detection approaches have a number of shortcomings, such as the inabilit... Read More about Helmet use detection of tracked motorcycles using CNN-based multi-task learning..

Topological optimization of the DenseNet with pretrained-weights inheritance and genetic channel selection. (2020)
Journal Article
FANG, Z, REN, J., MARSHALL, S., ZHAO, H., WANG, S. and LI, X. 2021. Topological optimization of the DenseNet with pretrained-weights inheritance and genetic channel selection. Pattern recognition [online], 109, article ID 107608. Available from: https://doi.org/10.1016/j.patcog.2020.107608

Convolutional neural networks (CNNs) have been successfully applied in many computer vision applications, especially in image classification tasks, where most of the structures have been designed manually. With the aid of skip connection and dense co... Read More about Topological optimization of the DenseNet with pretrained-weights inheritance and genetic channel selection..

Breast tumor segmentation in ultrasound images using contextual-information-aware deep adversarial learning framework. (2020)
Journal Article
SINGH, V.K., ABDEL-NASSER, M., AKRAM, F., RASHWAN, H.A., SARKER, M.M.K., PANDEY, N., ROMANI, S. and PUIG, D. 2020. Breast tumor segmentation in ultrasound images using contextual-information-aware deep adversarial learning framework. Expert systems with applications [online], 162, article 113870. Available from: https://doi.org/10.1016/j.eswa.2020.113870

Automatic tumor segmentation in breast ultrasound (BUS) images is still a challenging task because of many sources of uncertainty, such as speckle noise, very low signal-to-noise ratio, shadows that make the anatomical boundaries of tumors ambiguous,... Read More about Breast tumor segmentation in ultrasound images using contextual-information-aware deep adversarial learning framework..

Semisupervised hypergraph discriminant learning for dimensionality reduction of hyperspectral image. (2020)
Journal Article
LUO, F., GUO, T., LIN, Z., REN, J. and ZHOU, X. 2020. Semisupervised hypergraph discriminant learning for dimensionality reduction of hyperspectral image. IEEE journal of selected topics in applied earth observations and remote sensing [online], 13, pages 4242-4256. Available from: https://doi.org/10.1109/jstars.2020.3011431

Semisupervised learning is an effective technique to represent the intrinsic features of a hyperspectral image (HSI), which can reduce the cost to obtain the labeled information of samples. However, traditional semisupervised learning methods fail to... Read More about Semisupervised hypergraph discriminant learning for dimensionality reduction of hyperspectral image..

STEM teaching for the Internet of Things maker course: a teaching model based on the iterative loop. (2020)
Journal Article
CHEN, R., ZHENG, Y., XU, X., ZHAO, H., REN, J. and TAN, H.-Z. 2020. STEM teaching for the Internet of Things maker course: a teaching model based on the interative loop. Sustainability [online], 12(14), article 5758. Available from: https://doi.org/10.3390/su12145758

As the key technology for 5G applications in the future, the Internet of Things (IoT) is developing rapidly, and the demand for the cultivation of engineering talents in the IoT is also expanding. The rise of maker education has brought new teaching... Read More about STEM teaching for the Internet of Things maker course: a teaching model based on the iterative loop..

A novel spectral-spatial singular spectrum analysis technique for near real-time in-situ feature extraction in hyperspectral imaging. (2020)
Journal Article
FU, H., SUN, G., ZABALZA, J., ZHANG, A., REN, J. and JIA, X. 2020. A novel spectral-spatial singular spectrum analysis technique for near real-time in-situ feature extraction in hyperspectral imaging. IEEE journal of selected topics in applied earth observations and remote sensing [online], 13, pages 2214-2225. Available from: https://doi.org/10.1109/JSTARS.2020.2992230

As a cutting-edge technique for denoising and feature extraction, singular spectrum analysis (SSA) has been applied successfully for feature mining in hyperspectral images (HSI). However, when applying SSA for in situ feature extraction in HSI, conve... Read More about A novel spectral-spatial singular spectrum analysis technique for near real-time in-situ feature extraction in hyperspectral imaging..

Content-sensitive superpixel generation with boundary adjustment. (2020)
Journal Article
ZHANG, D., XIE, G., REN, J., ZHANG, Z., BAO, W. and XU, X. 2020. Content-sensitive superpixel generation with boundary adjustment. Applied sciences [online], 10(9), article 3150. Available from: https://doi.org/10.3390/app10093150

Superpixel segmentation has become a crucial tool in many image processing and computer vision applications. In this paper, a novel content-sensitive superpixel generation algorithm with boundary adjustment is proposed. First, the image local entropy... Read More about Content-sensitive superpixel generation with boundary adjustment..

Spatial residual blocks combined parallel network for hyperspectral image classification. (2020)
Journal Article
ZHANG, B., QING, C., XU, X. and REN, J. 2020. Spatial residual blocks combined parallel network for hyperspectral image classification. IEEE access [online], 8, pages 74513-74524. Available from: https://doi.org/10.1109/ACCESS.2020.2988553

In hyperspectral image (HSI) classification, there are challenges of the spatial variation in spectral features and the lack of labeled samples. In this paper, a novel spatial residual blocks combined parallel network (SRPNet) is proposed for HSI cla... Read More about Spatial residual blocks combined parallel network for hyperspectral image classification..

Health state classification of a spherical tank using a hybrid bag of features and K-nearest neighbor. (2020)
Journal Article
HASAN, M.J., KIM, J., KIM, C.H. and KIM, J.-M. 2020. Health state classification of a spherical tank using a hybrid bag of features and K-nearest neighbor. Applied sciences [online], 10(7), article 2525. Available from: https://doi.org/10.3390/app10072525

Feature analysis puts a great impact in determining the various health conditions of mechanical vessels. To achieve balance between traditional feature extraction and the automated feature selection process, a hybrid bag of features (HBoF) is designe... Read More about Health state classification of a spherical tank using a hybrid bag of features and K-nearest neighbor..

Adaptive distance-based band hierarchy (ADBH) for effective hyperspectral band selection. (2020)
Journal Article
SUN, H., REN, J., ZHAO, H., SUN, G., LIAO, W., FANG, Z. and ZABALZA, J. 2022. Adaptive distance-based band hierarchy (ADBH) for effective hyperspectral band selection. IEEE transactions on cybernetics [online], 52(1), pages 215-227. Available from: https://doi.org/10.1109/TCYB.2020.2977750

Band selection has become a significant issue for the efficiency of the hyperspectral image (HSI) processing. Although many unsupervised band selection (UBS) approaches have been developed in the last decades, a flexible and robust method is still la... Read More about Adaptive distance-based band hierarchy (ADBH) for effective hyperspectral band selection..

Urban PM2.5 concentration prediction via attention-based CNN–LSTM. (2020)
Journal Article
LI, S., XIE, G., REN, J., GUO, L., YANG, Y. and XU, X. 2020. Urban PM2.5 concentration prediction via attention-based CNN–LSTM. Applied sciences [online], 10(6), article 1953. Available from: https://doi.org/10.3390/app10061953

Urban particulate matter forecasting is regarded as an essential issue for early warning and control management of air pollution, especially fine particulate matter (PM2.5). However, existing methods for PM2.5 concentration prediction neglect the eff... Read More about Urban PM2.5 concentration prediction via attention-based CNN–LSTM..

Exemplar-supported representation for effective class-incremental learning. (2020)
Journal Article
GUO, L., XIE, G., XU, X. and REN, J. 2020. Exemplar-supported representation for effective class-incremental learning. IEEE access [online], 8, pages 51276-51284. Available from: https://doi.org/10.1109/ACCESS.2020.2980386

Catastrophic forgetting is a key challenge for class-incremental learning with deep neural networks, where the performance decreases considerably while dealing with long sequences of new classes. To tackle this issue, in this paper, we propose a new... Read More about Exemplar-supported representation for effective class-incremental learning..

Weakly supervised conditional random fields model for semantic segmentation with image patches. (2020)
Journal Article
XU, X., XUE, Y., HAN, X., ZHANG, Z., XIE, J. and REN, J. 2020. Weakly supervised conditional random fields model for semantic segmentation with image patches. Applied sciences [online], 10(5), article 1679. Available from: https://doi.org/10.3390/app10051679

Image semantic segmentation (ISS) is used to segment an image into regions with differently labeled semantic category. Most of the existing ISS methods are based on fully supervised learning, which requires pixel-level labeling for training the model... Read More about Weakly supervised conditional random fields model for semantic segmentation with image patches..

Heterogeneous parallelization for object detection and tracking in UAVs. (2020)
Journal Article
RABAH, M., ROHAN, A., HAGHBAYAN, M.-H., PLOSILA, J. and KIM, S.-H. 2020. Heterogeneous parallelization for object detection and tracking in UAVs. IEEE access [online], 8, pages 42784-42793. Available from: https://doi.org/10.1109/ACCESS.2020.2977120

Recent technical advancements in both fields of unmanned aerial vehicles (UAV) control and artificial intelligence (AI) have made a certain realm of applications possible. However, one of the main problems in integration of these two areas is the bot... Read More about Heterogeneous parallelization for object detection and tracking in UAVs..

Varietal classification of rice seeds using RGB and hyperspectral images. (2020)
Journal Article
FABIYI, S.D., VU, H., TACHTATZIS, C., MURRAY, P., HARLE, D., DAO, T.K., ANDONOVIC, I., REN, J. and MARSHALL, S. 2020. Varietal classification of rice seeds using RGB and hyperspectral images. IEEE access [online], 8, pages 22493-22505. Available from: https://doi.org/10.1109/ACCESS.2020.2969847

Inspection of rice seeds is a crucial task for plant nurseries and farmers since it ensures seed quality when growing seedlings. Conventionally, this process is performed by expert inspectors who manually screen large samples of rice seeds to identif... Read More about Varietal classification of rice seeds using RGB and hyperspectral images..