Professor ANDREW FRENCH's Outputs (29)

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.

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.

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.

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.

Enhancing supervised classifications with metamorphic relations (2018)
Presentation / Conference Contribution
Xu, L., Towey, D., French, A. P., Benford, S., Zhou, Z. Q., & Chen, T. Y. (2018, May). Enhancing supervised classifications with metamorphic relations. Presented at Proceedings of the 3rd International Workshop on Metamorphic Testing - MET '18

We report on a novel use of metamorphic relations (MRs) in machine learning: instead of conducting metamorphic testing, we use MRs for the augmentation of the machine learning algorithms themselves. In particular, we report on how MRs can enable enha... Read More about Enhancing supervised classifications with metamorphic relations.

Recognizing the Presence of Hidden Visual Markers in Digital Images (2017)
Presentation / Conference Contribution
Xu, L., French, A. P., Towey, D., & Benford, S. (2017, October). Recognizing the Presence of Hidden Visual Markers in Digital Images. Presented at Thematic Workshops of ACM Multimedia 2017, Mountain View, California, USA

As the promise of Virtual and Augmented Reality (VR and AR) becomes more realistic, an interesting aspect of our enhanced living environment includes the availability — indeed the potential ubiquity — of scannable markers. Such markers could represen... Read More about Recognizing the Presence of Hidden Visual Markers in Digital Images.

Deep learning for multi-task plant phenotyping (2017)
Presentation / Conference Contribution
Pound, M. P., Atkinson, J. A., Wells, D. M., Pridmore, T. P., & French, A. P. Deep learning for multi-task plant phenotyping. Presented at ICCV 2017 International Conference on Computer Vision, Venice, Italy

Plant phenotyping has continued to pose a challenge to computer vision for many years. There is a particular demand to accurately quantify images of crops, and the natural variability and structure of these plants presents unique difficulties. Recent... Read More about Deep learning for multi-task plant phenotyping.

Deep Learning for Multi-task Plant Phenotyping (2017)
Preprint / Working Paper
Pound, M. P., Atkinson, J. A., Wells, D. M., Pridmore, T. P., & French, A. P. (2017). Deep Learning for Multi-task Plant Phenotyping

Plant phenotyping has continued to pose a challenge to computer vision for many years. There is a particular demand to accurately quantify images of crops, and the natural variability and structure of these plants presents unique difficulties. Recent... Read More about Deep Learning for Multi-task Plant Phenotyping.

Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress (2017)
Journal Article
Lowe, A., Harrison, N., & French, A. P. (2017). Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress. Plant Methods, 13, Article 80. https://doi.org/10.1186/s13007-017-0233-z

This review explores how imaging techniques are being developed with a focus on deployment for crop monitoring methods. Imaging applications are discussed in relation to both field and glasshouse-based plants, and techniques are sectioned into ‘healt... Read More about Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress.

Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench (2017)
Journal Article
Darrow, M. C., Luengo, I., Basham, M., Spink, M. C., Irvine, S., French, A. P., Ashton, A. W., & Duke, E. M. (2017). Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench. Journal of Visualized Experiments, Article e56162. https://doi.org/10.3791/56162

Segmentation is the process of isolating specific regions or objects within an imaged volume, so that further study can be undertaken on these areas of interest. When considering the analysis of complex biological systems, the segmentation of three-d... Read More about Volume Segmentation and Analysis of Biological Materials Using SuRVoS (Super-region Volume Segmentation) Workbench.

Deep machine learning provides state-of-the-art performance in image-based plant phenotyping (2017)
Journal Article
Pound, M. P., Atkinson, J. A., Townsend, A. J., Wilson, M. H., Griffiths, M., Jackson, A. S., Bulat, A., Tzimiropoulos, G., Wells, D. M., Murchie, E. H., Pridmore, T. P., & French, A. P. (2017). Deep machine learning provides state-of-the-art performance in image-based plant phenotyping. GigaScience, 6(10), Article gix083. https://doi.org/10.1093/gigascience/gix083

© The Author 2017. In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, h... Read More about Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.

AutoRoot: open-source software employing a novel image analysis approach to support fully-automated plant phenotyping (2017)
Journal Article
Pound, M. P., Fozard, S., Torres Torres, M., Forde, B. G., & French, A. P. (2017). AutoRoot: open-source software employing a novel image analysis approach to support fully-automated plant phenotyping. Plant Methods, 13(1), Article 12. https://doi.org/10.1186/s13007-017-0161-y

Background: Computer-based phenotyping of plants has risen in importance in recent years. Whilst much software has been written to aid phenotyping using image analysis, to date the vast majority has been only semi-automatic. However, such interaction... Read More about AutoRoot: open-source software employing a novel image analysis approach to support fully-automated plant phenotyping.

The Microphenotron: a robotic miniaturized plant phenotyping platform with diverse applications in chemical biology (2017)
Journal Article
Burrell, T., Fozard, S., Holroyd, G. H., French, A. P., Pound, M. P., Bigley, C. J., James Taylor, C., & Forde, B. G. (2017). The Microphenotron: a robotic miniaturized plant phenotyping platform with diverse applications in chemical biology. Plant Methods, 13(1), Article 10. https://doi.org/10.1186/s13007-017-0158-6

Background
Chemical genetics provides a powerful alternative to conventional genetics for understanding gene function. However, its application to plants has been limited by the lack of a technology that allows detailed phenotyping of whole-seedling... Read More about The Microphenotron: a robotic miniaturized plant phenotyping platform with diverse applications in chemical biology.

SuRVoS: Super-Region Volume Segmentation workbench (2017)
Journal Article
Luengo, I., Darrow, M. C., Spink, M. C., Sun, Y., Dai, W., He, C. Y., Chiu, W., Pridmore, T., Ashton, A. W., Duke, E. M., Basham, M., & French, A. P. (2017). SuRVoS: Super-Region Volume Segmentation workbench. Journal of Structural Biology, 198(1), 43-53. https://doi.org/10.1016/j.jsb.2017.02.007

Segmentation of biological volumes is a crucial step needed to fully analyse their scientific content. Not having access to convenient tools with which to segment or annotate the data means many biological volumes remain under-utilised. Automatic seg... Read More about SuRVoS: Super-Region Volume Segmentation workbench.

SMURFS: Superpixels from multi-scale refinement of super-regions (2016)
Presentation / Conference Contribution
Luengo, I., Basham, M., & French, A. P. (2016, September). SMURFS: Superpixels from multi-scale refinement of super-regions. Presented at British Machine Vision Conference (BMVC 2016), York, UK

Recent applications in computer vision have come to rely on superpixel segmentation as a pre-processing step for higher level vision tasks, such as object recognition, scene labelling or image segmentation. Here, we present a new algorithm, Superpixe... Read More about SMURFS: Superpixels from multi-scale refinement of super-regions.