All Outputs (154)

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..

Gravitation-based edge detection in hyperspectral images (2017)
Journal Article
SUN, G., ZHANG, A., REN, J., MA, J., WANG, P., ZHANG, Y. and JIA, X. 2017. Gravitation-based edge detection in hyperspectral images. Remote sensing [online], 9(6), article 592. Available from: https://doi.org/10.3390/rs9060592

Edge detection is one of the key issues in the field of computer vision and remote sensing image analysis. Although many different edge-detection methods have been proposed for gray-scale, color, and multispectral images, they still face difficulties... Read More about Gravitation-based edge detection in hyperspectral images.

Evaluating urban land carrying capacity based on the ecological sensitivity analysis: a case study in Hangzhou, China. (2017)
Journal Article
TSOU, J.Y., GAO, Y., ZHANG, Y., SUN, G., REN, J. and LI, Y. 2017. Evaluating urban land carrying capacity based on the ecological sensitivity analysis: a case study in Hangzhou, China. Remote sensing [online], 9(6), article number 529. Available from: https://doi.org/10.3390/rs9060529

In this study, we present the evaluation of urban land carrying capacity (ULCC) based on an ecological sensitivity analysis. Remote sensing data and geographic information system (GIS) technology are employed to analyze topographic conditions, land-u... Read More about Evaluating urban land carrying capacity based on the ecological sensitivity analysis: a case study in Hangzhou, China..

Combining MLC and SVM classifiers for learning based decision making: analysis and evaluations. (2015)
Journal Article
ZHANG, Y., REN, J. and JIANG, J. 2015. Combining MLC and SVM classifiers for learning based decision making: analysis and evaluations. Computational intelligence and neuroscience [online], 2015, article ID 423581. Available from: https://doi.org/10.1155/2015/423581

Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonpara... Read More about Combining MLC and SVM classifiers for learning based decision making: analysis and evaluations..