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Outputs (19)

End-to-End Fovea Localisation in Colour Fundus Images with a Hierarchical Deep Regression Network (2020)
Journal Article
Xie, R., Liu, J., Cao, R., Qiu, C. S., Duan, J., Garibaldi, J., & Qiu, G. (2020). End-to-End Fovea Localisation in Colour Fundus Images with a Hierarchical Deep Regression Network. IEEE Transactions on Medical Imaging, 40(1), 116-128. https://doi.org/10.1109/TMI.2020.3023254

Accurately locating the fovea is a prerequisite for developing computer aided diagnosis (CAD) of retinal diseases. In colour fundus images of the retina, the fovea is a fuzzy region lacking prominent visual features and this makes it difficult to dir... Read More about End-to-End Fovea Localisation in Colour Fundus Images with a Hierarchical Deep Regression Network.

Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation (2019)
Book Chapter
Hou, X., Liu, J., Xu, B., Liu, B., Chen, X., Garibaldi, J., …Qiu, G. (2019). Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II (101-109). Springer Verlag. https://doi.org/10.1007/978-3-030-32245-8_12

Supervised semantic segmentation normally assumes the test data being in a similar data domain as the training data. However, in practice, the domain mismatch between the training and unseen data could lead to a significant performance drop. Obtainin... Read More about Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation.

Deep Reinforcement Learning based Patch Selection for Illuminant Estimation (2019)
Journal Article
Xu, B., Liu, J., Hou, X., Liu, B., & Qiu, G. (2019). Deep Reinforcement Learning based Patch Selection for Illuminant Estimation. Image and Vision Computing, 91, Article 103798. https://doi.org/10.1016/j.imavis.2019.08.002

Previous deep learning based approaches to illuminant estimation either resized the raw image to lower resolution or randomly cropped image patches for the deep learning model. However, such practices would inevitably lead to information loss or the... Read More about Deep Reinforcement Learning based Patch Selection for Illuminant Estimation.

Improving variational autoencoder with deep feature consistent and generative adversarial training (2019)
Journal Article
Hou, X., Sun, K., Shen, L., & Qiu, G. (2019). Improving variational autoencoder with deep feature consistent and generative adversarial training. Neurocomputing, 341, 183-194. https://doi.org/10.1016/j.neucom.2019.03.013

We present a new method for improving the performances of variational autoencoder (VAE). In addition to enforcing the deep feature consistent principle thus ensuring the VAE output and its corresponding input images to have similar deep features, we... Read More about Improving variational autoencoder with deep feature consistent and generative adversarial training.

Visual quality assessment for super-resolved images: database and method (2019)
Journal Article
Zhou, F., Yao, R., Liu, B., & Qiu, G. (2019). Visual quality assessment for super-resolved images: database and method. IEEE Transactions on Image Processing, 28(7), 3528-3541. https://doi.org/10.1109/tip.2019.2898638

Image super-resolution (SR) has been an active re-search problem which has recently received renewed interest due to the introduction of new technologies such as deep learning. However, the lack of suitable criteria to evaluate the SR perfor-mance ha... Read More about Visual quality assessment for super-resolved images: database and method.

Urban traffic density estimation based on ultrahigh-resolution UAV video and deep neural network (2018)
Journal Article
Zhu, J., Sun, K., Jia, S., Li, Q., Hou, X., Lin, W., …Qiu, G. (2018). Urban traffic density estimation based on ultrahigh-resolution UAV video and deep neural network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(12), 4968-4981. https://doi.org/10.1109/jstars.2018.2879368

This paper presents an advanced urban traffic density estimation solution using the latest deep learning techniques to intelligently process ultrahigh-resolution traffic videos taken from an unmanned aerial vehicle (UAV). We first capture nearly an h... Read More about Urban traffic density estimation based on ultrahigh-resolution UAV video and deep neural network.

A classification-regression deep learning model for people counting (2018)
Presentation / Conference Contribution
Xu, B., Zou, W., Garibaldi, J., & Qiu, G. (2018). A classification-regression deep learning model for people counting. In K. Arai, S. Kapoor, & R. Bhatia (Eds.), Intelligent Systems and Applications Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 1 (136-149). https://doi.org/10.1007/978-3-030-01054-6_9

In this paper, we construct a multi-task deep learning model to simultaneously predict people number and the level of crowd density. Motivated by the success of applying " ambiguous labelling " to age estimation problem, we also manage to employ this... Read More about A classification-regression deep learning model for people counting.

Integrating aerial and street view images for urban land use classification (2018)
Journal Article
Cao, R., Zhu, J., Tu, W., Li, Q., Cao, J., Liu, B., …Qiu, G. (2018). Integrating aerial and street view images for urban land use classification. Remote Sensing, 10(10), Article 1553. https://doi.org/10.3390/rs10101553

Urban land use is key to rational urban planning and management. Traditional land use classification methods rely heavily on domain experts, which is both expensive and inefficient. In this paper, deep neural network-based approaches are presented to... Read More about Integrating aerial and street view images for urban land use classification.

Learning based image transformation using convolutional neural networks (2018)
Journal Article
Hou, X., Gong, Y., Liu, B., Sun, K., Liu, J., Xu, B., …Qiu, G. (2018). Learning based image transformation using convolutional neural networks. IEEE Access, 6, 49779-49792. https://doi.org/10.1109/access.2018.2868733

We have developed a learning-based image transformation framework and successfully applied it to three common image transformation operations: downscaling, decolorization, and high dynamic range image tone mapping. We use a convolutional neural netwo... Read More about Learning based image transformation using convolutional neural networks.

An end-to-end deep learning histochemical scoring system for breast cancer TMA (2018)
Journal Article
Liu, J., Xu, B., Zheng, C., Gong, Y., Garibaldi, J., Soria, D., …Qiu, G. (2019). An end-to-end deep learning histochemical scoring system for breast cancer TMA. IEEE Transactions on Medical Imaging, 38(2), 617-628. https://doi.org/10.1109/TMI.2018.2868333

One of the methods for stratifying different molecular classes of breast cancer is the Nottingham prognostic index plus, which uses breast cancer relevant biomarkers to stain tumor tissues prepared on tissue microarray (TMA). To determine the molecul... Read More about An end-to-end deep learning histochemical scoring system for breast cancer TMA.

Direct Application of Convolutional Neural Network Features to Image Quality Assessment (2018)
Presentation / Conference Contribution
Hou, X., Sun, K., Liu, B., Gong, Y., Garibaldi, J., & Qiu, G. (2018). Direct Application of Convolutional Neural Network Features to Image Quality Assessment. In 2018 IEEE Visual Communications and Image Processing (VCIP). https://doi.org/10.1109/VCIP.2018.8698726

© 2018 IEEE. We take advantage of the popularity of deep con-volutional neural networks (CNNs) and have developed a very simple image quality assessment method that rivals state of the art. We show that convolutional layer outputs (deep features) of... Read More about Direct Application of Convolutional Neural Network Features to Image Quality Assessment.

Riemannian competitive learning for symmetric positive definite matrices clustering (2018)
Journal Article
Zheng, L., Qiu, G., & Huang, J. (2018). Riemannian competitive learning for symmetric positive definite matrices clustering. Neurocomputing, 295, 153-164. https://doi.org/10.1016/j.neucom.2018.03.015

Symmetric positive definite (SPD) matrices have achieved considerable success in numerous computer vision applications including activity recognition, texture classification, and diffusion tensor imaging. Traditional pattern recognition methods devel... Read More about Riemannian competitive learning for symmetric positive definite matrices clustering.

Luminance adaptive biomarker detection in digital pathology images (2016)
Journal Article
Liu, J., Qiu, G., & Shen, L. (in press). Luminance adaptive biomarker detection in digital pathology images. Procedia Computer Science, 90, https://doi.org/10.1016/j.procs.2016.07.032

Digital pathology is set to revolutionise traditional approaches diagnosing and researching diseases. To realise the full potential of digital pathology, accurate and robust computer techniques for automatically detecting biomarkers play an important... Read More about Luminance adaptive biomarker detection in digital pathology images.

Statistical colour models: an automated digital image analysis method for quantification of histological biomarkers (2016)
Journal Article
Shu, J., Dolman, G., Duan, J., Qiu, G., & Ilyas, M. (in press). Statistical colour models: an automated digital image analysis method for quantification of histological biomarkers. BioMedical Engineering OnLine, 15(1), https://doi.org/10.1186/s12938-016-0161-6

Background: Colour is the most important feature used in quantitative immunohisto- chemistry (IHC) image analysis; IHC is used to provide information relating to aetiology and to con rm malignancy. Methods: Statistical modelling is a technique widel... Read More about Statistical colour models: an automated digital image analysis method for quantification of histological biomarkers.

A novel polar space random field model for the detection of glandular structures (2014)
Journal Article
Fu, H., Qiu, G., Shu, J., & Ilyas, M. (2014). A novel polar space random field model for the detection of glandular structures. IEEE Transactions on Medical Imaging, 33(3), 764-776. https://doi.org/10.1109/TMI.2013.2296572

In this paper, we propose a novel method to detect glandular structures in microscopic images of human tissue. We first convert the image from Cartesian space to polar space and then introduce a novel random field model to locate the possible boundar... Read More about A novel polar space random field model for the detection of glandular structures.

Face hallucination based on sparse local-pixel structure (2013)
Journal Article
Li, Y., Cai, C., Qiu, G., & Lam, K.-M. (2014). Face hallucination based on sparse local-pixel structure. Pattern Recognition, 47(3), 1261-1270. https://doi.org/10.1016/j.patcog.2013.09.012

In this paper, we propose a face-hallucination method, namely face hallucination based on sparse local-pixel structure. In our framework, a high resolution (HR) face is estimated from a single frame low resolution (LR) face with the help of the facia... Read More about Face hallucination based on sparse local-pixel structure.