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All Outputs (17)

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.

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, 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.

Indoor Topological Localization Using a Visual Landmark Sequence (2019)
Journal Article
Zhu, J., Li, Q., Cao, R., Sun, K., Liu, T., Garibaldi, J., …Qiu, G. (2019). Indoor Topological Localization Using a Visual Landmark Sequence. Remote Sensing, 11(1), Article 73. https://doi.org/10.3390/rs11010073

© 2019 by the authors. This paper presents a novel indoor topological localization method based on mobile phone videos. Conventional methods suffer from indoor dynamic environmental changes and scene ambiguity. The proposed Visual Landmark Sequence-b... Read More about Indoor Topological Localization Using a Visual Landmark Sequence.

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.

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.

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.

Habitat image annotation with low-level features, medium-level knowledge and location information (2015)
Journal Article
Torres, M., & Qiu, G. (2016). Habitat image annotation with low-level features, medium-level knowledge and location information. Multimedia Systems, 22(6), 767-782. doi:10.1007/s00530-014-0445-2

The classification of habitats is crucial for structuring knowledge and developing our understanding of the natural world. Currently, most successful methods employ human surveyors—a laborious, expensive and subjective process. In this paper, we form... Read More about Habitat image annotation with low-level features, medium-level knowledge and location information.

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. (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.

Automatic habitat classification using image analysis and random forest (2013)
Journal Article
Torres, M., & Qiu, G. (2014). Automatic habitat classification using image analysis and random forest. Ecological Informatics, 23, 126-136. doi:10.1016/j.ecoinf.2013.08.002

Habitat classification is important for monitoring the environment and biodiversity. Currently, this is done manually by human surveyors, a laborious, expensive and subjective process. We have developed a new computer habitat classification method ba... Read More about Automatic habitat classification using image analysis and random forest.