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All Outputs (10)

Dynamic Unary Convolution in Transformers (2023)
Journal Article
Duan, H., Long, Y., Wang, S., Zhang, H., Willcocks, C. G., & Shao, L. (2023). Dynamic Unary Convolution in Transformers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(11), 12747 - 12759. https://doi.org/10.1109/tpami.2022.3233482

It is uncertain whether the power of transformer architectures can complement existing convolutional neural networks. A few recent attempts have combined convolution with transformer design through a range of structures in series, where the main cont... Read More about Dynamic Unary Convolution in Transformers.

Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models (2021)
Journal Article
Bond-Taylor, S., Leach, A., Long, Y., & Willcocks, C. G. (2021). Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11), 7327-7347. https://doi.org/10.1109/tpami.2021.3116668

Deep generative models are a class of techniques that train deep neural networks to model the distribution of training samples. Research has fragmented into various interconnected approaches, each of which make trade-offs including run-time, diversit... Read More about Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models.

Deep Learning Protein Conformational Space with Convolutions and Latent Interpolations (2021)
Journal Article
Ramaswamy, V. K., Musson, S. C., Willcocks, C. G., & Degiacomi, M. T. (2021). Deep Learning Protein Conformational Space with Convolutions and Latent Interpolations. Physical Review X, 11(1), Article 011052. https://doi.org/10.1103/physrevx.11.011052

Determining the different conformational states of a protein and the transition paths between them is key to fully understanding the relationship between biomolecular structure and function. This can be accomplished by sampling protein conformational... Read More about Deep Learning Protein Conformational Space with Convolutions and Latent Interpolations.

The relationship between curvilinear structure enhancement and ridge detection approaches (2020)
Journal Article
Alhasson, H., Willcocks, C. G., Alharbi, S. S., Kasim, A., & Obara, B. (2021). The relationship between curvilinear structure enhancement and ridge detection approaches. Visual Computer, 37(8), 2263-2283. https://doi.org/10.1007/s00371-020-01985-4

Curvilinear structure detection and quantification is a large research area with many imaging applications in fields such as biology, medicine, and engineering. Curvilinear enhancement is often used as a pre-processing stage for ridge detection, but... Read More about The relationship between curvilinear structure enhancement and ridge detection approaches.

Sequential graph-based extraction of curvilinear structures (2019)
Journal Article
Alharbi, S. S., Willcocks, C., Jackson, P. T., Alhasson, H. F., & Obara, B. (2019). Sequential graph-based extraction of curvilinear structures. Signal, Image and Video Processing, 13(5), 941-949. https://doi.org/10.1007/s11760-019-01431-6

In this paper, a new approach is proposed to extract an ordered sequence of curvilinear structures in images, capturing the largest and most influential paths first and then progressively extracting smaller paths until a prespecified size is reached.... Read More about Sequential graph-based extraction of curvilinear structures.

Using Deep Convolutional Neural Network Architectures for Object Classification and Detection within X-ray Baggage Security Imagery (2018)
Journal Article
Akcay, S., Kundegorski, M., Willcocks, C., & Breckon, T. (2018). Using Deep Convolutional Neural Network Architectures for Object Classification and Detection within X-ray Baggage Security Imagery. IEEE Transactions on Information Forensics and Security, 13(9), 2203-2215. https://doi.org/10.1109/tifs.2018.2812196

We consider the use of deep Convolutional Neural Networks (CNN) with transfer learning for the image classification and detection problems posed within the context of X-ray baggage security imagery. The use of the CNN approach requires large amounts... Read More about Using Deep Convolutional Neural Network Architectures for Object Classification and Detection within X-ray Baggage Security Imagery.

Interactive GPU Active Contours for Segmenting Inhomogeneous Objects (2017)
Journal Article
Willcocks, C. G., Jackson, P. T., Nelson, C. J., Nasrulloh, A., & Obara, B. (2019). Interactive GPU Active Contours for Segmenting Inhomogeneous Objects. Journal of Real-Time Image Processing, 16(6), 2305-2318. https://doi.org/10.1007/s11554-017-0740-1

We present a segmentation software package primarily targeting medical and biological applications, with a high level of visual feedback and several usability enhancements over existing packages. Specifically, we provide a substantially faster GPU im... Read More about Interactive GPU Active Contours for Segmenting Inhomogeneous Objects.

Multi-scale Segmentation and Surface Fitting for Measuring 3D Macular Holes (2017)
Journal Article
Nasrulloh, A., Willcocks, C., Jackson, P., Geenen, C., Habib, M., Steel, D., & Obara, B. (2018). Multi-scale Segmentation and Surface Fitting for Measuring 3D Macular Holes. IEEE Transactions on Medical Imaging, 37(2), 580-589. https://doi.org/10.1109/tmi.2017.2767908

Macular holes are blinding conditions where a hole develops in the central part of retina, resulting in reduced central vision. The prognosis and treatment options are related to a number of variables including the macular hole size and shape. High-r... Read More about Multi-scale Segmentation and Surface Fitting for Measuring 3D Macular Holes.

Extracting 3D parametric curves from 2D images of helical objects (2016)
Journal Article
Willcocks, C., Jackson, P. T., Nelson, C. J., & Obara, B. (2016). Extracting 3D parametric curves from 2D images of helical objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(9), 1757-1769. https://doi.org/10.1109/tpami.2016.2613866

Helical objects occur in medicine, biology, cosmetics, nanotechnology, and engineering. Extracting a 3D parametric curve from a 2D image of a helical object has many practical applications, in particular being able to extract metrics such as tortuosi... Read More about Extracting 3D parametric curves from 2D images of helical objects.