All Outputs (163)

Hyperspectral image reconstruction using multi-colour and time-multiplexed LED illumination. (2019)
Journal Article
TSCHANNERL, J., REN, J., ZHAO, H., KAO, F.-J., MARSHALL, S. and YUEN, P. 2019. Hyperspectral image reconstruction using multi-colour and time-multiplexed LED illumination. Optics and lasers in engineering [online], 121, pages 352-357. Available from: https://doi.org/10.1016/j.optlaseng.2019.04.014

The rapidly rising industrial interest in hyperspectral imaging (HSI) has generated an increased demand for cost effective HSI devices. We are demonstrating a mobile and low-cost multispectral imaging system, enabled by time-multiplexed RGB Light Emi... Read More about Hyperspectral image reconstruction using multi-colour and time-multiplexed LED illumination..

Coastal wetland mapping with sentinel-2 MSI imagery based on gravitational optimized multilayer perceptron and morphological attribute profiles. (2019)
Journal Article
ZHANG, A., SUN, G., MA, P., JIA, X., REN, J., HUANG, H. and ZHANG, X. 2019. Coastal wetland mapping with sentinel-2 MSI imagery based on gravitational optimized multilayer perceptron and morphological attribute profiles. Remote sensing [online], 11(8), article 952. Available from: https://doi.org/10.3390/rs11080952

Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and exten... Read More about Coastal wetland mapping with sentinel-2 MSI imagery based on gravitational optimized multilayer perceptron and morphological attribute profiles..

Building extraction from high-resolution aerial imagery using a generative adversarial network with spatial and channel attention mechanisms. (2019)
Journal Article
PAN, X., YANG, F., GAO, L., CHEN, Z., ZHANG, B., FAN, H. and REN, J. 2019. Building extraction from high-resolution aerial imagery using a generative adversarial network with spatial and channel attention mechanisms. Remote sensing [online], 11(8), article 917. Available from: https://doi.org/10.3390/rs11080917

Segmentation of high-resolution remote sensing images is an important challenge with wide practical applications. The increasing spatial resolution provides fine details for image segmentation but also incurs segmentation ambiguities. In this paper,... Read More about Building extraction from high-resolution aerial imagery using a generative adversarial network with spatial and channel attention mechanisms..

A new type of eye movement model based on recurrent neural networks for simulating the gaze behavior of human reading. (2019)
Journal Article
WANG, X., ZHAO, X. and REN, J. 2019. A new type of eye movement model based on recurrent neural networks for simulating the gaze behavior of human reading. Complexity [online], 2019: complex deep learning and evolutionary computing models in computer vision, article ID 8641074. Available from: https://doi.org/10.1155/2019/8641074

Traditional eye movement models are based on psychological assumptions and empirical data that are not able to simulate eye movement on previously unseen text data. To address this problem, a new type of eye movement model is presented and tested in... Read More about A new type of eye movement model based on recurrent neural networks for simulating the gaze behavior of human reading..

EEG-based brain-computer interfaces using motor-imagery: techniques and challenges. (2019)
Journal Article
PADFIELD, N., ZABALZA, J., ZHAO, H., MASERO, V. and REN, J. 2019. EEG-based brain-computer interfaces using motor-imagery: techniques and challenges. Sensors [online], 19(6), article 1423. Available from: https://doi.org/10.3390/s19061423

Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a s... Read More about EEG-based brain-computer interfaces using motor-imagery: techniques and challenges..

Local block multilayer sparse extreme learning machine for effective feature extraction and classification of hyperspectral images. (2019)
Journal Article
CAO, F., YANG, Z., REN, J., CHEN, W., HAN, G. and SHEN, Y. 2019. Local block multilayer sparse extreme learning machine for effective feature extraction and classification of hyperspectral images. IEEE transactions on geoscience and remote sensing [online], 57(8), pages 5580-5594. Available from: https://doi.org/10.1109/tgrs.2019.2900509

Although extreme learning machines (ELM) have been successfully applied for the classification of hyperspectral images (HSIs), they still suffer from three main drawbacks. These include: 1) ineffective feature extraction (FE) in HSIs due to a single... Read More about Local block multilayer sparse extreme learning machine for effective feature extraction and classification of hyperspectral images..

Smart sensing and adaptive reasoning for enabling industrial robots with interactive human-robot capabilities in dynamic environments: a case study. (2019)
Journal Article
ZABALZA, J., FEI, Z., WONG, C., YAN, Y., MINEO, C., YANG, E., RODDEN, T., MEHNEN, J., PHAM, Q.-C. and REN, J. 2019. Smart sensing and adaptive reasoning for enabling industrial robots with interactive human-robot capabilities in dynamic environments: a case study. Sensors [online], 19(6), article 1354. Available from: https://doi.org/10.3390/s19061354

Traditional industry is seeing an increasing demand for more autonomous and flexible manufacturing in unstructured settings, a shift away from the fixed, isolated workspaces where robots perform predefined actions repetitively. This work presents a c... Read More about Smart sensing and adaptive reasoning for enabling industrial robots with interactive human-robot capabilities in dynamic environments: a case study..

Weakly supervised deep semantic segmentation using CNN and ELM with semantic candidate regions. (2019)
Journal Article
XU, X., LI, G., XIE, G., REN, J. and XIE, X. 2019. Weakly supervised deep semantic segmentation using CNN and ELM with semantic candidate regions. Complexity [online], 2019: complex deep learning and evolutionary computing models in computer vision, article 9180391. Available from: https://doi.org/10.1155/2019/9180391

The task of semantic segmentation is to obtain strong pixel-level annotations for each pixel in the image. For fully supervised semantic segmentation, the task is achieved by a segmentation model trained using pixel-level annotations. However, the pi... Read More about Weakly supervised deep semantic segmentation using CNN and ELM with semantic candidate regions..

Superpixel based feature specific sparse representation for spectral-spatial classification of hyperspectral images. (2019)
Journal Article
SUN, H., REN, J., ZHAO, H., YAN, Y., ZABALZA, J. and MARSHALL, S. 2019. Superpixel based feature specific sparse representation for spectral-spatial classification of hyperspectral images. Remote sensing [online], 11(5), article 536. Available from: https://doi.org/10.3390/rs11050536

To improve the performance of the sparse representation classification (SRC), we propose a superpixel-based feature specific sparse representation framework (SPFS-SRC) for spectral-spatial classification of hyperspectral images (HSI) at superpixel le... Read More about Superpixel based feature specific sparse representation for spectral-spatial classification of hyperspectral images..

Automatic image segmentation with superpixels and image-level labels. (2019)
Journal Article
XIE, X., XIE, G., XU, X., CUI, L. and REN, J. 2019. Automatic image segmentation with superpixels and image-level labels. IEEE access [online], 7, pages 10999-11009. Available from: https://doi.org/10.1109/ACCESS.2019.2891941

Automatically and ideally segmenting the semantic region of each object in an image will greatly improve the precision and efficiency of subsequent image processing. We propose an automatic image segmentation algorithm based on superpixels and image-... Read More about Automatic image segmentation with superpixels and image-level labels..

Making industrial robots smarter with adaptive reasoning and autonomous thinking for real-time tasks in dynamic environments: a case study. (2018)
Presentation / Conference Contribution
ZABALZA, J., FEI, Z., WONG, C., YAN, Y., MINEO, C., YANG, E., RODDEN, T., MEHNEN, J., PHAM, Q.-C. and REN, J. 2018. Making industrial robots smarter with adaptive reasoning and autonomous thinking for real-time tasks in dynamic environments: a case study. In Ren, J., Hussain, A., Zheng, J., Liu, C.-L., Lou, B., Zhao, H. and Zhao, X. (eds.) Advances in brain inspired cognitive systems: proceedings of 9th International conference on Brain inspired cognitive system 2018 (BICS2018), 7-8 July 2018, Xi'an, China. Lecture notes in computer science, 10989. Cham: Springer [online], pages 790-800. Available from: https://doi.org/10.1007/978-3-030-00563-4_77

In order to extend the abilities of current robots in industrial applications towards more autonomous and flexible manufacturing, this work presents an integrated system comprising real-time sensing, path-planning and control of industrial robots to... Read More about Making industrial robots smarter with adaptive reasoning and autonomous thinking for real-time tasks in dynamic environments: a case study..

Deep Learning Based Single Image Super-Resolution: A Survey (2018)
Presentation / Conference Contribution
HA, V.K., REN, J., XU, X., ZHAO, S. XIE, G. and VARGAS, V.M. 2018. Deep learning based single image super-resolution: a survey. In Ren, J., Hussain, A., Zheng, J., Liu, C.-L., Luo, B., Zhao, H. and Zhao, X. (eds.) Advances in brain inspired cognitive systems: proceedings of 9th International conference brain inspired cognitive systems 2018 (BICS 2018), 7-8 July 2018, Xi'an, China. Lecture notes in computer sciences, 10989. Cham: Springer [online], pages 106-119. Available from: https://doi.org/10.1007/978-3-030-00563-4_11

Image super-resolution is a process of obtaining one or more high-resolution image from single or multiple samples of low-resolution images. Due to its wide applications, a number of different techniques have been developed recently, including interp... Read More about Deep Learning Based Single Image Super-Resolution: A Survey.

Class imbalance ensemble learning based on the margin theory. (2018)
Journal Article
FENG, W., HUANG, W. and REN, J. 2018. Class imbalance ensemble learning based on the margin theory. Applied sciences [online], 8(5), article number 815. Available from: https://doi.org/10.3390/app8050815

The proportion of instances belonging to each class in a data-set plays an important role in machine learning. However, the real world data often suffer from class imbalance. Dealing with multi-class tasks with different misclassification costs of cl... Read More about Class imbalance ensemble learning based on the margin theory..

Coherent narrow-band light source for miniature endoscopes. (2018)
Journal Article
CHEN, Z.-Y., GOGOI, A., LEE, S.-Y., TSAI-LIN, Y., YI, P.-W.Y., LU, M.-K., HSIEH, C.-C., REN, J., MARSHALL, S. and KAO, F.-J. 2019. Coherent narrow-band light source for miniature endoscopes. IEEE journal of selected topics in quantum electronics [online], 25(1), article 7100707. Available from: https://doi.org/10.1109/JSTQE.2018.2836959

In this work, we report the successful implementation of a coherent narrow-band light source for miniature endoscopy applications. An RGB laser module that provides much higher luminosity than traditional incoherent white light sources is used for il... Read More about Coherent narrow-band light source for miniature endoscopes..

Sparse representation-based augmented multinomial logistic extreme learning machine with weighted composite features for spectral–spatial classification of hyperspectral images. (2018)
Journal Article
CAO, F., YANG, Z., REN, J., LING, W.-K., ZHAO, H., SUN, M. and BENEDIKTSSON, J.A. 2018. Sparse representation-based augmented multinomial logistic extreme learning machine with weighted composite features for spectral–spatial classification of hyperspectral images. IEEE transactions on geoscience and remote sensing [online], 56(11), pages 6263-6279. Available from: https://doi.org/10.1109/tgrs.2018.2828601

Although extreme learning machine (ELM) has successfully been applied to a number of pattern recognition problems, only with the original ELM it can hardly yield high accuracy for the classification of hyperspectral images (HSIs) due to two main draw... Read More about Sparse representation-based augmented multinomial logistic extreme learning machine with weighted composite features for spectral–spatial classification of hyperspectral images..

Unsupervised image saliency detection with Gestalt-laws guided optimization and visual attention based refinement. (2018)
Journal Article
YAN, Y., REN, J., SUN, G., ZHAO, H., HAN, J., LI, X., MARSHALL, S. and ZHAN, J. 2018. Unsupervised image saliency detection with Gestalt-laws guided optimization and visual attention based refinement. Pattern recognition [online], 79, pages 65-78. Available from: https://doi.org/10.1016/j.patcog.2018.02.004

Visual attention is a kind of fundamental cognitive capability that allows human beings to focus on the region of interests (ROIs) under complex natural environments. What kind of ROIs that we pay attention to mainly depends on two distinct types of... Read More about Unsupervised image saliency detection with Gestalt-laws guided optimization and visual attention based refinement..

Extreme sparse multinomial logistic regression: a fast and robust framework for hyperspectral image classification. (2017)
Journal Article
CAO, F., YANG, Z., REN, J., LING, W.-K., ZHAO, H. and MARSHALL, S. 2017. Extreme sparse multinomial logistic regression: a fast and robust framework for hyperspectral image classification. Remote sensing [online], 9(12), article number 1255. Available from: https://doi.org/10.3390/rs9121255

Although sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constr... Read More about Extreme sparse multinomial logistic regression: a fast and robust framework for hyperspectral image classification..

Hyperspectral image enhancement and mixture deep-learning classification of corneal epithelium injuries. (2017)
Journal Article
NOOR, S.S.M., MICHAEL, K., MARSHALL, S. and REN, J. 2017. Hyperspectral image enhancement and mixture deep-learning classification of corneal epithelium injuries. Sensors [online], 17(11), article number 2644. Available from: https://doi.org/10.3390/s17112644

In our preliminary study, the reflectance signatures obtained from hyperspectral imaging (HSI) of normal and abnormal corneal epithelium tissues of porcine show similar morphology with subtle differences. Here we present image enhancement algorithms... Read More about Hyperspectral image enhancement and mixture deep-learning classification of corneal epithelium injuries..

Linear vs. nonlinear extreme learning machine for spectral-spatial classification of hyperspectral images. (2017)
Journal Article
CAO, F., YANG, Z., REN, J., JIANG, M. and LING, W.-K. 2017. Linear vs. nonlinear extreme learning machine for spectral-spatial classification of hyperspectral images. Sensors [online], 17(11), article number 2603. Available from: https://doi.org/10.3390/s17112603

As a new machine learning approach, the extreme learning machine (ELM) has received much attention due to its good performance. However, when directly applied to hyperspectral image (HSI) classification, the recognition rate is low. This is because E... Read More about Linear vs. nonlinear extreme learning machine for spectral-spatial classification of hyperspectral images..

Dynamic post-earthquake image segmentation with an adaptive spectral-spatial descriptor. (2017)
Journal Article
SUN, G., HAO, Y., CHEN, X., REN, J., ZHANG, A., HUANG, B., ZHANG, Y. and JIA, X. 2017. Dynamic post-earthquake image segmentation with an adaptive spectral-spatial descriptor. Remote sensing [online], 9(9), article number 899. Available from: https://doi.org/10.3390/rs9090899

The region merging algorithm is a widely used segmentation technique for very high resolution (VHR) remote sensing images. However, the segmentation of post-earthquake VHR images is more difficult due to the complexity of these images, especially hig... Read More about Dynamic post-earthquake image segmentation with an adaptive spectral-spatial descriptor..