Michael P. Pound

发表

A patch-based approach to 3D plant shoot phenotyping

Tony P. Pridmore, Michael P. Pound, Andrew P. French, 2016, Machine Vision and Applications.

Three-dimensional reconstruction of plant shoots from multiple images using an active vision system

Tony P. Pridmore, Michael P. Pound, Andrew P. French, 2020 .

Deep Learning for Multitask Plant Phenotyping

Tony P. Pridmore, Michael P. Pound, Andrew P. French, 2017 .

Deep machine learning provides state-of-the-art performance in image-based plant phenotyping

Tony P. Pridmore, Michael P. Pound, Andrew P. French, 2016, bioRxiv.

Deep Hourglass for Brain Tumor Segmentation

Tony P. Pridmore, Michael P. Pound, Andrew P. French, 2018, BrainLes@MICCAI.

Three Dimensional Root CT Segmentation using Multi-Resolution Encoder-Decoder Networks

Tony P. Pridmore, Michael P. Pound, Mohammadreza Soltaninejad, 2019, bioRxiv.

Tracking and identifying groups of moving targets using probabilistic parsing

Michael P. Pound, 2010 .

Deep Learning for Multi-task Plant Phenotyping

Tony P. Pridmore, Michael P. Pound, Andrew P. French, 2017, bioRxiv.

RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures

Tony P. Pridmore, Michael P. Pound, Darren M. Wells, 2019, bioRxiv.

Pressing the Flesh: Sensing Multiple Touch and Finger Pressure on Arbitrary Surfaces

Tony P. Pridmore, Joe Marshall, Steve Benford, 2009, Pervasive.

The effect of depth context in the segmentation of the colon in MRI volumes

Tony P. Pridmore, Michael P. Pound, Andrew P. French, 2020, medRxiv.

Surface Reconstruction of Plant Shoots from Multiple Views

Tony P. Pridmore, Michael P. Pound, Andrew P. French, 2014, ECCV Workshops.

RootNav: Navigating Images of Complex Root Architectures1[C][W]

Michael P. Pound, Andrew P. French, Darren M. Wells, 2013, Plant Physiology.

Analysis of root growth using image analysis

Michael P. Pound, Andrew P. French, A. French, 2014 .

AutoRoot: open-source software employing a novel image analysis approach to support fully-automated plant phenotyping

Michael P. Pound, Andrew P. French, Susan Fozard, 2017, Plant Methods.

Deep Learning for Multi-task Plant Phenotyping

Tony P. Pridmore, Michael P. Pound, Andrew P. French, 2017 .

Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping

Tony P. Pridmore, Michael P. Pound, Andrew P. French, 2016 .

Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields

Tony P. Pridmore, Michael P. Pound, Andrew P. French, 2020, Plant Methods.

Addressing Multiple Salient Object Detection via Dual-Space Long-Range Dependencies pdf

Michael P. Pound, Andrew P. French, Bowen Deng, 2021, ArXiv.

Learning to Localise and Count with Incomplete Dot-annotations

Michael P. Pound, Andrew P. French, Feng Chen, 2021, 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).

Predicting Plant Growth from Time-Series Data Using Deep Learning

Michael P. Pound, Robail Yasrab, Jincheng Zhang, 2021, Remote. Sens..

Quantitative and Qualitative Evaluation of Visual Tracking Algorithms using Statistical Tests

Michael P. Pound, A. French, T. Pridmore, 2007 .

Approaches to three-dimensional reconstruction of plant shoot topology and geometry.

Michael P. Pound, A. French, T. Pridmore, 2016, Functional plant biology : FPB.

Systems Analysis of Auxin Transport in the Arabidopsis Root Apex[W][OPEN]

Michael P. Pound, John A. Fozard, Michael H. Wilson, 2014, Plant Cell.

Gibbs, Jonathon and Pound, Michael P. and Wells, Darren M. and Murchie, Erik H. and French, Andrew P. and Pridmore, Tony P. (2015) Three-dimensional reconstruction of plant shoots from multiple images using an active vision system. In: Agri-Food Robotics

Michael P. Pound, A. French, T. Pridmore, 2017 .

Phenotyping pipeline reveals major seedling root growth QTL in hexaploid wheat

Michael P. Pound, Darren M. Wells, Simon Griffiths, 2015, Journal of experimental botany.

Analysis of root growth using image analysis

Michael P. Pound, Andrew P. French, 2014 .

Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning

Michael P. Pound, A. French, U. Voß, 2020, Frontiers in Plant Science.

Integration of hormonal signaling networks and mobile microRNAs is required for vascular patterning in Arabidopsis roots

Christophe Godin, Michael P. Pound, Daniele Muraro, 2013, Proceedings of the National Academy of Sciences.

X‐ray CT reveals 4D root system development and lateral root responses to nitrate in soil

Michael P. Pound, S. Mooney, T. Pridmore, 2022, The Plant Phenome Journal.

The 4-Dimensional Plant: Effects of Wind-Induced Canopy Movement on Light Fluctuations and Photosynthesis

Tony P. Pridmore, Michael P. Pound, Erik H. Murchie, 2016, Front. Plant Sci..

Image-based 3D canopy reconstruction to determine potential productivity in complex multi-species crop systems

Michael P. Pound, Erik H. Murchie, Sean Mayes, 2017, Annals of botany.

CellSeT: Novel Software to Extract and Analyze Structured Networks of Plant Cells from Confocal Images[W]

Tony P. Pridmore, Michael P. Pound, Andrew P. French, 2012, Plant Cell.

What lies beneath: underlying assumptions in bioimage analysis.

Michael P. Pound, Tony P Pridmore, Michael P Pound, 2012, Trends in plant science.

Quantification of fluorescent reporters in plant cells.

Michael P. Pound, A. French, D. Wells, 2015, Methods in molecular biology.

High-Resolution Three-Dimensional Structural Data Quantify the Impact of Photoinhibition on Long-Term Carbon Gain in Wheat Canopies in the Field1[OPEN]

Tony P. Pridmore, Michael P. Pound, Erik H. Murchie, 2015, Plant Physiology.

Volumetric Segmentation of Cell Cycle Markers in Confocal Images

Michael P. Pound, A. French, U. Voß, 2019, bioRxiv.

The Microphenotron: a robotic miniaturized plant phenotyping platform with diverse applications in chemical biology

Michael P. Pound, A. French, B. Forde, 2017, Plant Methods.

Pound, Michael P. and French, Andrew P. and Fozard, John A. and Murchie, Erik H. and Pridmore, Tony P. (2016) A patch-based approach to 3D plant shoot phenotyping. Machine Vision and Applications . ISSN 1432-1769

Michael P. Pound, John A. Fozard, A. French, 2016 .

Identification of QTL and Underlying Genes for Root System Architecture associated with Nitrate Nutrition in Hexaploid Wheat

Michael P. Pound, Michael H. Wilson, J. Atkinson, 2019, bioRxiv.

A review of ultrasonic sensing and machine learning methods to monitor industrial processes.

Michael P. Pound, N. Watson, Alexander L. Bowler, 2022, Ultrasonics.

Running head : Automated recovery of 3 D models of plant shoots Corresponding

Michael P. Pound, A. French, T. Pridmore, 2014 .

Recovering Wind-induced Plant Motion in Dense Field Environments 3 via Deep Learning and Multiple Object Tracking 4 5 6

Michael P. Pound, T. Pridmore, Murchie, 2019 .

High-resolution 3D structural data quantifies the impact of photoinhibition on long term carbon gain in wheat canopies in the field

Michael P. Pound, O. Jensen, R. Retkute, 2015 .

Burgess, Alexandra J. and Retkute, Renata and Preston, Simon P. and Jensen, Oliver E. and Pound, Michael P. and Pridmore, Tony P. and Murchie, Erik H. (2016) The 4-dimensional plant: effects of wind-induced canopy movement on light fluctuations

Michael P. Pound, T. Pridmore, O. Jensen, 2017 .

Recovering Wind-Induced Plant Motion in Dense Field Environments via Deep Learning and Multiple Object Tracking1[CC-BY]

Michael P. Pound, Jonathon Ashley Gibbs, Tony P Pridmore, 2019, Plant Physiology.

Pound, Michael P. and Fozard, Susan and Torres Torres, Mercedes and Forde, Brian G. and French, Andrew P. (2017) AutoRoot: open-source software employing a novel image analysis approach to support fully-

Michael P. Pound, A. French, Mercedes Torres Torres, 2018 .

The Microphenotron: a robotic miniaturized plant phenotyping platform with diverse applications in chemical biology

Michael P. Pound, A. French, B. Forde, 2017, Plant Methods.

Plant Phenotyping: An Active Vision Cell for Three-Dimensional Plant Shoot Reconstruction1[OPEN]

Michael P. Pound, Tony Pridmore, Darren M Wells, 2018, Plant Physiology.

Uncovering the hidden half of plants using new advances in root phenotyping

Michael P. Pound, Jonathan A Atkinson, Darren M Wells, 2019, Current opinion in biotechnology.

The 6xABRE Synthetic Promoter Enables the Spatiotemporal Analysis of ABA-Mediated Transcriptional Regulation1[OPEN]

Michael P. Pound, S. Kay, José L. Pruneda-Paz, 2018, Plant Physiology.

Identification of nitrogen-dependent QTL and underlying genes for root system architecture in hexaploid wheat

Michael P. Pound, Michael H. Wilson, J. Atkinson, 2019 .

Root System Markup Language: Toward a Unified Root Architecture Description Language1[OPEN]

Mathieu Javaux, Michael P. Pound, Xavier Draye, 2015, Plant Physiology.

Active Vision and Surface Reconstruction for 3D Plant Shoot Modelling

Michael P. Pound, Jonathon Ashley Gibbs, Tony Pridmore, 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Automated Recovery of Three-Dimensional Models of Plant Shoots from Multiple Color Images1[C][W][OPEN]

Michael P. Pound, Tony P Pridmore, Michael P Pound, 2014, Plant Physiology.

CNN based Heuristic Function for A* Pathfinding Algorithm: Using Spatial Vector Data to Reconstruct Smooth and Natural Looking Plant Roots

Michael P. Pound, R. Yasrab, 2021, bioRxiv.

PhenomNet: Bridging Phenotype-Genotype Gap: A CNN-LSTM Based Automatic Plant Root Anatomization System

Michael P. Pound, Tony P Pridmore, Robail Yasrab, 2020, bioRxiv.

Efficient MRI Brain Tumor Segmentation Using Multi-resolution Encoder-Decoder Networks

Michael P. Pound, T. Pridmore, M. Soltaninejad, 2020, BrainLes@MICCAI.

GANana: Unsupervised Domain Adaptation for Volumetric Regression of Fruit

Michael P. Pound, Aaron S. Jackson, A. French, 2021, Plant phenomics.

AutoRoot: open-source software employing a novel image analysis approach to support fully-automated plant phenotyping

Michael P. Pound, A. French, Mercedes Torres Torres, 2017, Plant Methods.

Domain Adaptation and Federated Learning for Ultrasonic Monitoring of Beer Fermentation

Michael P. Pound, N. Watson, A. Bowler, 2021, Fermentation.

Convolutional feature extraction for process monitoring using ultrasonic sensors

Michael P. Pound, Michael Pound, Alexander Bowler, 2021, Comput. Chem. Eng..

Breakthrough Technologies RootNav : Navigating Images of Complex Root Architectures 1 [ C ] [ W ]

Michael P. Pound, A. French, T. Pridmore, 2013 .

Quantitative Hormone Signaling Output Analyses of Arabidopsis thaliana Interactions With Virulent and Avirulent Hyaloperonospora arabidopsidis Isolates at Single-Cell Resolution

Michael P. Pound, I. Feussner, V. Lipka, 2020, Frontiers in Plant Science.

Root System Markup Language: Toward a Unified Root Architecture Description Language1[OPEN]

Michael P. Pound, T. Pridmore, X. Draye, 2015, Plant Physiology.

An introduction to images and image analysis

Michael P. Pound, A. French, 2014 .

RootNet: A Convolutional Neural Networks for Complex Plant Root Phenotyping from High-Definition Datasets

Michael P. Pound, Tony P Pridmore, Robail Yasrab, 2020, bioRxiv.

Deep Learning for Plant Phenotyping

Michael P. Pound, Michael Pound, 2017 .

(2017) Deep learning for multi-task plant phenotyping. In: ICCV 2017 International Conference on Computer Vision, 22-29 October, 2017, Venice, Italy

Michael P. Pound, A. French, T. Pridmore, 2017 .