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Professor GUOPING QIU's Outputs (16)

Relative geometry-aware siamese neural network for 6DOF camera relocalization (2020)
Journal Article
Li, Q., Zhu, J., Cao, R., Sun, K., Garibaldi, J. M., Li, Q., Liu, B., & Qiu, G. (2021). Relative geometry-aware siamese neural network for 6DOF camera relocalization. Neurocomputing, 426, 134-146. https://doi.org/10.1016/j.neucom.2020.09.071

6DOF camera relocalization is an important component of autonomous driving and navigation. Deep learning has recently emerged as a promising technique to tackle this problem. In this paper, we present a novel relative geometry-aware Siamese neural ne... Read More about Relative geometry-aware siamese neural network for 6DOF camera relocalization.

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)
Presentation / Conference Contribution
Hou, X., Liu, J., Xu, B., Liu, B., Chen, X., Garibaldi, J., Ilyas, M., Ellis, I., & Qiu, G. (2019, October). Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation. Presented at 22nd International Conference, Shenzhen, China

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.

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., Liu, B., & 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., Zhang, Q., & 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., Duan, J., & 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.

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, December). Direct Application of Convolutional Neural Network Features to Image Quality Assessment. Presented at IEEE Visual Communications and Image Processing (VCIP 2018), Taichung, Taiwan

© 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.

Visual landmark sequence-based indoor localization (2017)
Presentation / Conference Contribution
Li, Q., Zhu, J., Liu, T., Garibaldi, J., Li, Q., & Qiu, G. (2017, November). Visual landmark sequence-based indoor localization. Presented at 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery, Los Angeles, California, USA

This paper presents a method that uses common objects as landmarks for smartphone-based indoor localization and navigation. First, a topological map marking relative positions of common objects such as doors, stairs and toilets is generated from floo... Read More about Visual landmark sequence-based indoor localization.

Crowd-sourcing applied to photograph-based automatic habitat classification (2014)
Presentation / Conference Contribution
Torres Torres, M., & Qiu, G. (2014, November). Crowd-sourcing applied to photograph-based automatic habitat classification. Presented at MAED 2014 - Proceedings of the 3rd ACM International Regular and Data Challenge Workshop on Multimedia Analysis for Ecological Data, Orlando Florida USA

Habitat classification is a crucial activity for monitoring environmental biodiversity. To date, manual methods, which are laborious, time-consuming and expensive, remain the most successful alternative. Most automatic methods use remote-sensed image... Read More about Crowd-sourcing applied to photograph-based automatic habitat classification.

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.

Supervised learning and anti-learning of colorectal cancer classes and survival rates from cellular biology parameters
Presentation / Conference Contribution
Roadknight, C., Aickelin, U., Qiu, G., Scholefield, J., & Durrant, L. Supervised learning and anti-learning of colorectal cancer classes and survival rates from cellular biology parameters. Presented at 2012 IEEE International Conference on Systems, Man and Cybernetics - SMC

In this paper, we describe a dataset relating to cellular
and physical conditions of patients who are operated upon to
remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal,tumour cl... Read More about Supervised learning and anti-learning of colorectal cancer classes and survival rates from cellular biology parameters.