Outputs (10)

Temporal neighbourhood aggregation: predicting future links in temporal graphs via recurrent variational graph convolutions (2019)
Presentation / Conference Contribution
Bonner, S., Atapour-Abarghouei, A., Jackson, P., Brennan, J., Kureshi, I., Theodoropoulos, G., …Obara, B. (2019). Temporal neighbourhood aggregation: predicting future links in temporal graphs via recurrent variational graph convolutions. In 2019 IEEE International Conference on Big Data (Big Data) (5336-5345). https://doi.org/10.1109/bigdata47090.2019.9005545

Graphs have become a crucial way to represent large, complex and often temporal datasets across a wide range of scientific disciplines. However, when graphs are used as input to machine learning models, this rich temporal information is frequently di... Read More about Temporal neighbourhood aggregation: predicting future links in temporal graphs via recurrent variational graph convolutions.

Coarse annotation refinement for segmentation of dot-matrix batchcodes (2019)
Presentation / Conference Contribution
Jia, N., Holder, C., Bonner, S., & Obara, B. (2019). Coarse annotation refinement for segmentation of dot-matrix batchcodes. In Proceedings of the 18th IEEE International Conference on Machine Learning and Applications (2001-2007). https://doi.org/10.1109/icmla.2019.00320

Deep Convolutional Neural Networks (CNN) have been extensively applied in various computer vision tasks. Although such approaches have demonstrated exceptionally high performance in various open challenges, adapting them to more specialised tasks can... Read More about Coarse annotation refinement for segmentation of dot-matrix batchcodes.

On the use of neural text generation for the task of optical character recognition (2019)
Presentation / Conference Contribution
Mohammadi, M., Jaf, S., Breckon, T., Matthews, P., McGough, A. S., Theodoropoulos, G., & Obara, B. (2019). On the use of neural text generation for the task of optical character recognition. In 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA) (1-8). https://doi.org/10.1109/aiccsa47632.2019.9035333

Optical Character Recognition (OCR), is extraction of textual data from scanned text documents to facilitate their indexing, searching, editing and to reduce storage space. Although OCR systems have improved significantly in recent years, they still... Read More about On the use of neural text generation for the task of optical character recognition.

Analysing social media as a hybrid tool to detect and interpret likely radical behavioural traits for national security (2019)
Presentation / Conference Contribution
Cardenas-Canto, P., Theodoropoulos, G., Obara, B., & Kureshi, I. (2019). Analysing social media as a hybrid tool to detect and interpret likely radical behavioural traits for national security. In Proceedings of the IEEE International Conference on Big Data (Human-in-the-loop Methods and Human Machine Collaboration in BigData) (4579-4588). https://doi.org/10.1109/bigdata47090.2019.9006259

The study of National Security and its associated considerations is a sensitive and complex paradigm. It encapsulates both the protection of the territorial integrity and sovereignty of a state, as well as guaranteeing the security of its population.... Read More about Analysing social media as a hybrid tool to detect and interpret likely radical behavioural traits for national security.

Combining mathematical morphology and the Hilbert transform for fully automatic nuclei detection in fluorescence microscopy (2019)
Presentation / Conference Contribution
Nelson, C. J., Jackson, P. T., & Obara, B. (2019, August). Combining mathematical morphology and the Hilbert transform for fully automatic nuclei detection in fluorescence microscopy. Presented at The International Symposium on Mathematical Morphology, Saarbrucken, Germany

Exploring the semantic content of unsupervised graph embeddings: an empirical study (2019)
Journal Article
Bonner, S., Kureshi, I., Brennan, J., Theodoropoulos, G., McGough, S., & Obara, B. (2019). Exploring the semantic content of unsupervised graph embeddings: an empirical study. Data Science and Engineering, 4(3), 269-289. https://doi.org/10.1007/s41019-019-0097-5

Graph embeddings have become a key and widely used technique within the field of graph mining, proving to be successful across a broad range of domains including social, citation, transportation and biological. Unsupervised graph embedding techniques... Read More about Exploring the semantic content of unsupervised graph embeddings: an empirical study.

The multiscale top-hat tensor enables specific enhancement of curvilinear structures in 2D and 3D images (2019)
Journal Article
Alharbi, S. S., Sazak, C., Alhasson, H., Nelson, C. J., & Obara, B. (2020). The multiscale top-hat tensor enables specific enhancement of curvilinear structures in 2D and 3D images. Methods, 173, 3-15. https://doi.org/10.1016/j.ymeth.2019.05.025

Quantification and modelling of curvilinear structures in 2D and 3D images is a common challenge in a wide range of biomedical applications. Image enhancement is a crucial pre-processing step for curvilinear structure quantification. Many of the exis... Read More about The multiscale top-hat tensor enables specific enhancement of curvilinear structures in 2D and 3D images.

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.

Style Augmentation: Data Augmentation via Style Randomization (2019)
Presentation / Conference Contribution
Jackson, P., Atapour-Abarghouei, A., Bonner, S., Breckon, T., & Obara, B. (2019). Style Augmentation: Data Augmentation via Style Randomization.

We introduce style augmentation, a new form of data augmentation based on random style transfer, for improving the robustness of Convolutional Neural Networks (CNN) over both classification and regression based tasks. During training, style augmentat... Read More about Style Augmentation: Data Augmentation via Style Randomization.

Phenotypic profiling of high throughput imaging screens with generic deep convolutional features (2019)
Presentation / Conference Contribution
Jackson, P., Wang, Y., Bendtsen, C., Knight, S., Chen, H., Dorval, T., …Obara, B. (2019). Phenotypic profiling of high throughput imaging screens with generic deep convolutional features.