Outputs (34)

High-fidelity wheat plant reconstruction using 3D Gaussian splatting and neural radiance fields (2025)
Journal Article
Pound, M. P., Stuart, L. A., Wells, D. M., Atkinson, J. A., Castle-Green, S., & Walker, J. (2025). High-fidelity wheat plant reconstruction using 3D Gaussian splatting and neural radiance fields. GigaScience, 14, Article giaf022. https://doi.org/10.1093/gigascience/giaf022

Background: The reconstruction of 3-dimensional (3D) plant models can offer advantages over traditional 2-dimensional approaches by more accurately capturing the complex structure and characteristics of different crops. Conventional 3D reconstruction... Read More about High-fidelity wheat plant reconstruction using 3D Gaussian splatting and neural radiance fields.

Practical aberration correction using deep transfer learning with limited experimental data (2025)
Journal Article
Kok, Y. E., Bentley, A., Parkes, A. J., Somekh, M. G., Wright, A. J., & Pound, M. P. (2025). Practical aberration correction using deep transfer learning with limited experimental data. Optics Express, 33(6), 14431-14444. https://doi.org/10.1364/oe.557993

Adaptive optics is a technique for correcting aberrations and improving image quality. When adaptive optics was first used in microscopy, it was common to rely on iterative approaches to determine the aberrations present. It is advantageous to avoid... Read More about Practical aberration correction using deep transfer learning with limited experimental data.

Supporting data for "High-fidelity Wheat Plant Reconstruction using 3D Gaussian Splatting and Neural Radiance Fields" (2025)
Data
(2025). Supporting data for "High-fidelity Wheat Plant Reconstruction using 3D Gaussian Splatting and Neural Radiance Fields". [Data]. https://doi.org/10.5524/102661

The reconstruction of 3D plant models can offer advantages over traditional 2D approaches by more accurately capturing the complex structure and characteristics of different crops. Conventional 3D reconstruction techniques often produce sparse or noi... Read More about Supporting data for "High-fidelity Wheat Plant Reconstruction using 3D Gaussian Splatting and Neural Radiance Fields".

Advancing Saliency Ranking with Human Fixations: Dataset, Models and Benchmarks (2024)
Presentation / Conference Contribution
Deng, B., Song, S., French, A. P., Schluppeck, D., & Pound, M. P. (2024, June). Advancing Saliency Ranking with Human Fixations: Dataset, Models and Benchmarks. Presented at Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA

Saliency ranking detection (SRD) has emerged as a challenging task in computer vision, aiming not only to identify salient objects within images but also to rank them based on their degree of saliency. Existing SRD datasets have been created primaril... Read More about Advancing Saliency Ranking with Human Fixations: Dataset, Models and Benchmarks.

Classification of osteoarthritic and healthy cartilage using deep learning with Raman spectra (2024)
Journal Article
Kok, Y. E., Crisford, A., Parkes, A., Venkateswaran, S., Oreffo, R., Mahajan, S., & Pound, M. (2024). Classification of osteoarthritic and healthy cartilage using deep learning with Raman spectra. Scientific Reports, 14(1), Article 15902. https://doi.org/10.1038/s41598-024-66857-6

Raman spectroscopy is a rapid method for analysing the molecular composition of biological material. However, noise contamination in the spectral data necessitates careful pre-processing prior to analysis. Here we propose an end-to-end Convolutional... Read More about Classification of osteoarthritic and healthy cartilage using deep learning with Raman spectra.

Domain Targeted Synthetic Plant Style Transfer using Stable Diffusion, LoRA and ControlNet (2024)
Presentation / Conference Contribution
Hartley, Z. K., Lind, R. J., Pound, M. P., & French, A. P. (2024, June). Domain Targeted Synthetic Plant Style Transfer using Stable Diffusion, LoRA and ControlNet. Presented at 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA

Synthetic images can help alleviate much of the cost in the creation of training data for plant phenotyping-focused AI development. Synthetic-to-real style transfer is of particular interest to users of artificial data because of the domain shift pro... Read More about Domain Targeted Synthetic Plant Style Transfer using Stable Diffusion, LoRA and ControlNet.

Root architecture and leaf photosynthesis traits and associations with nitrogen-use efficiency in landrace-derived lines in wheat (2022)
Journal Article
Kareem, S. H., Hawkesford, M. J., DeSilva, J., Weerasinghe, M., Wells, D. M., Pound, M. P., Atkinson, J. A., & Foulkes, M. J. (2022). Root architecture and leaf photosynthesis traits and associations with nitrogen-use efficiency in landrace-derived lines in wheat. European Journal of Agronomy, 140, Article 126603. https://doi.org/10.1016/j.eja.2022.126603

Root system architecture (RSA) is important in optimizing the use of nitrogen. High-throughput phenotyping techniques may be used to study root system architecture traits under controlled environments. A root phenotyping platform, consisting of germi... Read More about Root architecture and leaf photosynthesis traits and associations with nitrogen-use efficiency in landrace-derived lines in wheat.

Evaluation of synthetic aerial imagery using unconditional generative adversarial networks (2022)
Journal Article
Yates, M., Hart, G., Houghton, R., Torres Torres, M., & Pound, M. (2022). Evaluation of synthetic aerial imagery using unconditional generative adversarial networks. ISPRS Journal of Photogrammetry and Remote Sensing, 190, 231-251. https://doi.org/10.1016/j.isprsjprs.2022.06.010

Image generation techniques, such as generative adversarial networks (GANs), have become sufficiently sophisticated to cause growing concerns around the authenticity of images in the public domain. Although these generation techniques have been appli... Read More about Evaluation of synthetic aerial imagery using unconditional generative adversarial networks.

Identification of QTL and underlying genes for root system architecture associated with nitrate nutrition in hexaploid wheat (2022)
Journal Article
GRIFFITHS, M., ATKINSON, J. A., Gardiner, L. J., SWARUP, R., POUND, M. P., WILSON, M. H., BENNETT, M. J., & WELLS, D. M. (2022). Identification of QTL and underlying genes for root system architecture associated with nitrate nutrition in hexaploid wheat. Journal of Integrative Agriculture, 21(4), 917-932. https://doi.org/10.1016/s2095-3119%2821%2963700-0

The root system architecture (RSA) of a crop has a profound effect on the uptake of nutrients and consequently the potential yield. However, little is known about the genetic basis of RSA and resource adaptive responses in wheat (Triticum aestivum L.... Read More about Identification of QTL and underlying genes for root system architecture associated with nitrate nutrition in hexaploid wheat.

A fusion spatial attention approach for few-shot learning (2021)
Journal Article
Song, H., Deng, B., Pound, M., Özcan, E., & Triguero, I. (2022). A fusion spatial attention approach for few-shot learning. Information Fusion, 81, 187-202. https://doi.org/10.1016/j.inffus.2021.11.019

Few-shot learning is a challenging problem in computer vision that aims to learn a new visual concept from very limited data. A core issue is that there is a large amount of uncertainty introduced by the small training set. For example, the few image... Read More about A fusion spatial attention approach for few-shot learning.