Professor ANDREW FRENCH's Outputs (51)

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.

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.

Unlocking Comparative Plant Scoring with Siamese Neural Networks and Pairwise Pseudo Labelling (2023)
Presentation / Conference Contribution
Hartley, Z. K., Lind, R. J., Smith, N., Collison, B., & French, A. P. (2023, October). Unlocking Comparative Plant Scoring with Siamese Neural Networks and Pairwise Pseudo Labelling. Presented at 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Paris, France

Phenotypic assessment of plants for herbicide discovery is a complex visual task and involves the comparison of a non-treated plant to those treated with herbicides to assign a phytotoxicity score. It is often subjective and difficult to quantify by... Read More about Unlocking Comparative Plant Scoring with Siamese Neural Networks and Pairwise Pseudo Labelling.

Addressing multiple salient object detection via dual-space long-range dependencies (2023)
Journal Article
Deng, B., French, A. P., & Pound, M. P. (2023). Addressing multiple salient object detection via dual-space long-range dependencies. Computer Vision and Image Understanding, 235, Article 103776. https://doi.org/10.1016/j.cviu.2023.103776

Salient object detection plays an important role in many downstream tasks. However, complex real-world scenes with varying scales and numbers of salient objects still pose a challenge. In this paper, we directly address the problem of detecting multi... Read More about Addressing multiple salient object detection via dual-space long-range dependencies.

Semantic Segmentation of Spontaneous Intracerebral Hemorrhage, Intraventricular Hemorrhage, and Associated Edema on CT Images Using Deep Learning (2022)
Journal Article
Kok, Y. E., Pszczolkowski, S., Law, Z. K., Ali, A., Krishnan, K., Bath, P., Sprigg, N., Dineen, R. A., & French, A. P. (2022). Semantic Segmentation of Spontaneous Intracerebral Hemorrhage, Intraventricular Hemorrhage, and Associated Edema on CT Images Using Deep Learning. Radiology: Artificial Intelligence, 4(6), https://doi.org/10.1148/ryai.220096

This study evaluated deep learning algorithms for semantic segmentation and quantification of intracerebral hemorrhage (ICH), perihematomal edema (PHE), and intraventricular hemorrhage (IVH) on noncontrast CT scans of patients with spontaneous ICH. M... Read More about Semantic Segmentation of Spontaneous Intracerebral Hemorrhage, Intraventricular Hemorrhage, and Associated Edema on CT Images Using Deep Learning.

Temporal refinement of 3D CNN semantic segmentations on 4D time-series of undersampled tomograms using hidden Markov models (2021)
Journal Article
Bellos, D., Basham, M., Pridmore, T., & French, A. P. (2021). Temporal refinement of 3D CNN semantic segmentations on 4D time-series of undersampled tomograms using hidden Markov models. Scientific Reports, 11(1), Article 23279. https://doi.org/10.1038/s41598-021-02466-x

Recently, several convolutional neural networks have been proposed not only for 2D images, but also for 3D and 4D volume segmentation. Nevertheless, due to the large data size of the latter, acquiring a sufficient amount of training annotations is mu... Read More about Temporal refinement of 3D CNN semantic segmentations on 4D time-series of undersampled tomograms using hidden Markov models.

Domain Adaptation of Synthetic Images for Wheat Head Detection (2021)
Journal Article
Hartley, Z. K., & French, A. P. (2021). Domain Adaptation of Synthetic Images for Wheat Head Detection. Plants, 10(12), Article 2633. https://doi.org/10.3390/plants10122633

Wheat head detection is a core computer vision problem related to plant phenotyping that in recent years has seen increased interest as large-scale datasets have been made available for use in research. In deep learning problems with limited training... Read More about Domain Adaptation of Synthetic Images for Wheat Head Detection.

A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms (2021)
Journal Article
Bellos, D., Basham, M., Pridmore, T., & French, A. P. (2021). A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms. Machine Vision and Applications, 32(3), Article 75. https://doi.org/10.1007/s00138-021-01196-4

Over recent years, many approaches have been proposed for the denoising or semantic segmentation of X-ray computed tomography (CT) scans. In most cases, high-quality CT reconstructions are used; however, such reconstructions are not always available.... Read More about A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms.

Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning (2020)
Journal Article
Khan, F. A., Voß, U., Pound, M. P., & French, A. P. (2020). Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning. Frontiers in Plant Science, 11, Article 1275. https://doi.org/10.3389/fpls.2020.01275

© Copyright © 2020 Khan, Voß, Pound and French. Understanding plant growth processes is important for many aspects of biology and food security. Automating the observations of plant development—a process referred to as plant phenotyping—is increasing... Read More about Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning.

Towards infield, live plant phenotyping using a reduced-parameter CNN (2019)
Journal Article
Atanbori, J., French, A. P., & Pridmore, T. P. (2020). Towards infield, live plant phenotyping using a reduced-parameter CNN. Machine Vision and Applications, 31, Article 2. https://doi.org/10.1007/s00138-019-01051-7

There is an increase in consumption of agricultural produce as a result of the rapidly growing human population, particularly in developing nations. This has triggered high-quality plant phenotyping re- search to help with the breeding of high yieldi... Read More about Towards infield, live plant phenotyping using a reduced-parameter CNN.

CNN-Based Cassava Storage Root Counting Using Real and Synthetic Images (2019)
Journal Article
Atanbori, J., Montoya, M., Selvaraj, M., French, A. P., & Pridmore, T. P. (2019). CNN-Based Cassava Storage Root Counting Using Real and Synthetic Images. Frontiers in Plant Science, 10, Article 1516. https://doi.org/10.3389/fpls.2019.01516

Cassava roots are complex structures comprising several distinct types of root. The number and size of the storage roots are two potential phenotypic traits reflecting crop yield and quality. Counting and measuring the size of cassava storage roots a... Read More about CNN-Based Cassava Storage Root Counting Using Real and Synthetic Images.

A low-cost aeroponic phenotyping system for storage root development: Unravelling the below-ground secrets of cassava (Manihot esculenta) (2019)
Journal Article
Selvaraj, M. G., Montoya-P, M. E., Atanbori, J., French, A. P., & Pridmore, T. (2019). A low-cost aeroponic phenotyping system for storage root development: Unravelling the below-ground secrets of cassava (Manihot esculenta). Plant Methods, 15(1), Article 131. https://doi.org/10.1186/s13007-019-0517-6

© 2019 The Author(s). Background: Root and tuber crops are becoming more important for their high source of carbohydrates, next to cereals. Despite their commercial impact, there are significant knowledge gaps about the environmental and inherent reg... Read More about A low-cost aeroponic phenotyping system for storage root development: Unravelling the below-ground secrets of cassava (Manihot esculenta).

RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures (2019)
Journal Article
Yasrab, R., Atkinson, J. A., Wells, D. M., French, A. P., Pridmore, T. P., & Pound, M. P. (2019). RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures. GigaScience, 8(11), Article giz123. https://doi.org/10.1093/gigascience/giz123

BACKGROUND: In recent years quantitative analysis of root growth has become increasingly important as a way to explore the influence of abiotic stress such as high temperature and drought on a plant's ability to take up water and nutrients. Segmentat... Read More about RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures.

A convolutional neural network for fast upsampling of undersampled tomograms in X-ray CT time-series using a representative highly sampled tomogram (2019)
Journal Article
Bellos, D., Basham, M., Pridmore, T., & French, A. P. (2019). A convolutional neural network for fast upsampling of undersampled tomograms in X-ray CT time-series using a representative highly sampled tomogram. Journal of Synchrotron Radiation, 26(3), 839-853. https://doi.org/10.1107/s1600577519003448

We designed a convolutional neural network to quickly and accurately upscale the sinograms of x-ray tomograms captured with a low number of projections; effectively increasing the number of projections. This is particularly useful for tomograms that... Read More about A convolutional neural network for fast upsampling of undersampled tomograms in X-ray CT time-series using a representative highly sampled tomogram.

Active Vision and Surface Reconstruction for 3D Plant Shoot Modelling (2019)
Journal Article
Gibbs, J., French, A., Murchie, E., Wells, D., Pound, M., & Pridmore, T. (2020). Active Vision and Surface Reconstruction for 3D Plant Shoot Modelling. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(6), 1907-1917. https://doi.org/10.1109/TCBB.2019.2896908

Plant phenotyping is the quantitative description of a plant’s physiological, biochemical and anatomical status which can be used in trait selection and helps to provide mechanisms to link underlying genetics with yield. Here, an active vision- based... Read More about Active Vision and Surface Reconstruction for 3D Plant Shoot Modelling.

Deep Hourglass for Brain Tumor Segmentation (2019)
Presentation / Conference Contribution
Benson, E., Pound, M. P., French, A. P., Jackson, A. S., & Pridmore, T. P. (2018, September). Deep Hourglass for Brain Tumor Segmentation. Presented at 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain

The segmentation of a brain tumour in an MRI scan is a challenging task, in this paper we present our results for this problem via the BraTS 2018 challenge, consisting of 210 high grade glioma (HGG) and 75 low grade glioma (LGG) volumes for training.... Read More about Deep Hourglass for Brain Tumor Segmentation.

Roots branch towards water by post-translational modification of transcription factor ARF7 (2018)
Journal Article
Orosa Puente, B., Leftley, N., Von Wangenheim, D., Banda, J., Anjil, S., Hill, K., Truskina, J., Bhosale, R., Morris, E., Srivastava, M., Kumpers, B., Goh, T., Fukaki, H., Vermeer, J. E., Vernoux, T., Dinneny, J. R., French, A. P., Bishopp, A., Sadanandom, A., & Bennett, M. (2018). Roots branch towards water by post-translational modification of transcription factor ARF7. Science, 362(6421), 1407-1410. https://doi.org/10.1126/science.aau3956

Plants adapt to heterogeneous soil conditions by altering their root architecture. For example, roots branch when in contact with water using the hydropatterning response. We report that hydropatterning is dependent on auxin response factor ARF7. Thi... Read More about Roots branch towards water by post-translational modification of transcription factor ARF7.

Plant phenotyping: an active vision cell for three-dimensional plant shoot reconstruction (2018)
Journal Article
Gibbs, J., Pound, M., French, A. P., Wells, D. M., Murchie, E., & Pridmore, T. (2018). Plant phenotyping: an active vision cell for three-dimensional plant shoot reconstruction. Plant Physiology, 178(2), 524-534. https://doi.org/10.1104/pp.18.00664

© 2018 American Society of Plant Biologists. All rights reserved. Three-dimensional (3D) computer-generated models of plants are urgently needed to support both phenotyping and simulation-based studies such as photosynthesis modeling. However, the co... Read More about Plant phenotyping: an active vision cell for three-dimensional plant shoot reconstruction.

Towards low-cost image-based plant phenotyping using reduced-parameter CNN (2018)
Presentation / Conference Contribution
Atanbori, J., Chen, F., French, A. P., & Pridmore, T. (2018, September). Towards low-cost image-based plant phenotyping using reduced-parameter CNN. Presented at CVPPP 2018: Workshop on Computer Vision Problems in Plant Phenotyping

Segmentation is the core of most plant phenotyping applications. Current state-of-the-art plant phenotyping applications rely on deep Convolutional Neural Networks (CNNs). However, these networks have many layers and parameters, increasing training a... Read More about Towards low-cost image-based plant phenotyping using reduced-parameter CNN.

Cellular patterning of Arabidopsis roots under low phosphate conditions (2018)
Journal Article
Janes, G., von Wangenheim, D., Cowling, S., Kerr, I. D., Band, L. R., French, A. P., & Bishopp, A. (2018). Cellular patterning of Arabidopsis roots under low phosphate conditions. Frontiers in Plant Science, 9, Article 735. https://doi.org/10.3389/fpls.2018.00735

Phosphorus is a crucial macronutrient for plants playing a critical role in many cellular signaling and energy cycling processes. In light of this, phosphorus acquisition efficiency is an important target trait for crop improvement, but it also provi... Read More about Cellular patterning of Arabidopsis roots under low phosphate conditions.