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MICHAEL POUND


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.

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., …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.

A review of ultrasonic sensing and machine learning methods to monitor industrial processes (2022)
Journal Article
Bowler, A. L., Pound, M. P., & Watson, N. J. (2022). A review of ultrasonic sensing and machine learning methods to monitor industrial processes. Ultrasonics, 124, Article 106776. https://doi.org/10.1016/j.ultras.2022.106776

Supervised machine learning techniques are increasingly being combined with ultrasonic sensor measurements owing to their strong performance. These techniques also offer advantages over calibration procedures of more complex fitting, improved general... Read More about A review of ultrasonic sensing and machine learning methods to monitor industrial processes.

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., …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.

X-ray CT reveals 4D root system development and lateral root responses to nitrate in soil (2022)
Journal Article
Griffiths, M., Mellor, N., Sturrock, C. J., Atkinson, B. S., Johnson, J., Mairhofer, S., …Wells, D. M. (2022). X-ray CT reveals 4D root system development and lateral root responses to nitrate in soil. Plant Phenome Journal, 5(1), Article e20036. https://doi.org/10.1002/ppj2.20036

The spatial arrangement of the root system, termed root system architecture, is important for resource acquisition as it directly affects the soil zone explored. Methods for phenotyping roots are mostly destructive, which prevents analysis of roots o... Read More about X-ray CT reveals 4D root system development and lateral root responses to nitrate in soil.

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.

Domain adaptation and federated learning for ultrasonic monitoring of beer fermentation (2021)
Journal Article
Bowler, A. L., Pound, M. P., & Watson, N. J. (2021). Domain adaptation and federated learning for ultrasonic monitoring of beer fermentation. Fermentation, 7(4), Article 253. https://doi.org/10.3390/fermentation7040253

Beer fermentation processes are traditionally monitored through sampling and off-line wort density measurements. In-line and on-line sensors would provide real-time data on the fermentation progress whilst minimising human involvement, enabling ident... Read More about Domain adaptation and federated learning for ultrasonic monitoring of beer fermentation.

Convolutional feature extraction for process monitoring using ultrasonic sensors (2021)
Journal Article
Bowler, A., Pound, M., & Watson, N. (2021). Convolutional feature extraction for process monitoring using ultrasonic sensors. Computers and Chemical Engineering, 155, Article 107508. https://doi.org/10.1016/j.compchemeng.2021.107508

Ultrasonic sensors are a low-cost and in-line technique and can be combined with machine learning for industrial process monitoring. However, training accurate machine learning models for process monitoring using sensor data is dependant on the featu... Read More about Convolutional feature extraction for process monitoring using ultrasonic sensors.

Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning (2021)
Journal Article
Bowler, A., Escrig, J., Pound, M., & Watson, N. (2021). Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning. Fermentation, 7(1), Article 34. https://doi.org/10.3390/fermentation7010034

Beer fermentation is typically monitored by periodic sampling and off-line analysis. In-line sensors would remove the need for time-consuming manual operation and provide real-time evaluation of the fermenting media. This work uses a low-cost ultraso... Read More about Predicting Alcohol Concentration during Beer Fermentation Using Ultrasonic Measurements and Machine Learning.

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.