Professor ANDREW FRENCH's Outputs (9)

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.