Michael Pound | Academic Influence

Michael Pound's AcademicInfluence.com Rankings

Michael Pound

Computer Science

#10230

World Rank

#10726

Historical Rank

Database

#8247

World Rank

#8608

Historical Rank

computer-science Degrees

Download Badge

Computer Science

Michael Pound's Degrees

PhD Computer Science University of Nottingham
Masters Computer Science University of Nottingham
Bachelors Computer Science University of Nottingham

Similar Degrees You Can Earn

Why Is Michael Pound Influential?

(Suggest an Edit or Addition)

According to Wikipedia, Michael P. Pound is a researcher at the University of Nottingham. He is known for his work in the areas of bioimage analysis, computer vision, image recognition, computer security, and for his appearances on the video series Computerphile.

(See a Problem?)

Michael Pound's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Deep machine learning provides state-of-the-art performance in image-based plant phenotyping (2016) (259)
Uncovering the hidden half of plants using new advances in root phenotyping (2019) (208)
Systems Analysis of Auxin Transport in the Arabidopsis Root Apex[W][OPEN] (2014) (179)
RootNav: Navigating Images of Complex Root Architectures1[C][W] (2013) (172)
Phenotyping pipeline reveals major seedling root growth QTL in hexaploid wheat (2015) (168)
Deep Learning for Multi-task Plant Phenotyping (2017) (114)
Automated Recovery of Three-Dimensional Models of Plant Shoots from Multiple Color Images1[C][W][OPEN] (2014) (107)
Integration of hormonal signaling networks and mobile microRNAs is required for vascular patterning in Arabidopsis roots (2013) (99)
Root System Markup Language: Toward a Unified Root Architecture Description Language1[OPEN] (2015) (99)
CellSeT: Novel Software to Extract and Analyze Structured Networks of Plant Cells from Confocal Images[W] (2012) (81)
Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields (2020) (70)
RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures (2019) (70)
Approaches to three-dimensional reconstruction of plant shoot topology and geometry. (2016) (66)
The 4-Dimensional Plant: Effects of Wind-Induced Canopy Movement on Light Fluctuations and Photosynthesis (2016) (54)
The 6xABRE Synthetic Promoter Enables the Spatiotemporal Analysis of ABA-Mediated Transcriptional Regulation1[OPEN] (2018) (42)
High-Resolution Three-Dimensional Structural Data Quantify the Impact of Photoinhibition on Long-Term Carbon Gain in Wheat Canopies in the Field1[OPEN] (2015) (40)
Pressing the Flesh: Sensing Multiple Touch and Finger Pressure on Arbitrary Surfaces (2009) (33)
Plant Phenotyping: An Active Vision Cell for Three-Dimensional Plant Shoot Reconstruction1[OPEN] (2018) (32)
Image-based 3D canopy reconstruction to determine potential productivity in complex multi-species crop systems (2017) (31)
Three Dimensional Root CT Segmentation using Multi-Resolution Encoder-Decoder Networks (2019) (30)
What lies beneath: underlying assumptions in bioimage analysis. (2012) (27)
A patch-based approach to 3D plant shoot phenotyping (2016) (26)
Predicting Plant Growth from Time-Series Data Using Deep Learning (2021) (25)
Deep Hourglass for Brain Tumor Segmentation (2018) (24)
The Microphenotron: a robotic miniaturized plant phenotyping platform with diverse applications in chemical biology (2017) (18)
Deep Learning for Multitask Plant Phenotyping (2017) (15)
Active Vision and Surface Reconstruction for 3D Plant Shoot Modelling (2020) (15)
AutoRoot: open-source software employing a novel image analysis approach to support fully-automated plant phenotyping (2017) (14)
Recovering Wind-Induced Plant Motion in Dense Field Environments via Deep Learning and Multiple Object Tracking1[CC-BY] (2019) (11)
PhenomNet: Bridging Phenotype-Genotype Gap: A CNN-LSTM Based Automatic Plant Root Anatomization System (2020) (11)
Erratum to: Deep machine learning provides state-of-the-art performance in image-based plant phenotyping (2018) (10)
Surface Reconstruction of Plant Shoots from Multiple Views (2014) (10)
High-resolution 3D structural data quantifies the impact of photoinhibition on long term carbon gain in wheat canopies in the field (2015) (8)
Identification of QTL and Underlying Genes for Root System Architecture associated with Nitrate Nutrition in Hexaploid Wheat (2019) (7)
X‐ray CT reveals 4D root system development and lateral root responses to nitrate in soil (2022) (7)
RootNet: A Convolutional Neural Networks for Complex Plant Root Phenotyping from High-Definition Datasets (2020) (7)
A review of ultrasonic sensing and machine learning methods to monitor industrial processes. (2022) (5)
An introduction to images and image analysis (2014) (5)
Efficient MRI Brain Tumor Segmentation Using Multi-resolution Encoder-Decoder Networks (2020) (5)
Domain Adaptation and Federated Learning for Ultrasonic Monitoring of Beer Fermentation (2021) (4)
Quantitative and Qualitative Evaluation of Visual Tracking Algorithms using Statistical Tests (2007) (4)
Volumetric Segmentation of Cell Cycle Markers in Confocal Images Using Machine Learning and Deep Learning (2019) (4)
GANana: Unsupervised Domain Adaptation for Volumetric Regression of Fruit (2021) (4)
Analysis of root growth using image analysis (2014) (3)
Quantitative Hormone Signaling Output Analyses of Arabidopsis thaliana Interactions With Virulent and Avirulent Hyaloperonospora arabidopsidis Isolates at Single-Cell Resolution (2020) (3)
Convolutional feature extraction for process monitoring using ultrasonic sensors (2021) (3)
Deep Learning for Plant Phenotyping (2017) (2)
The Microphenotron: a robotic miniaturized plant phenotyping platform with diverse applications in chemical biology (2017) (1)
Root architecture and leaf photosynthesis traits and associations with nitrogen-use efficiency in landrace-derived lines in wheat (2022) (1)
Three-dimensional reconstruction of plant shoots from multiple images using an active vision system (2020) (1)
Learning to Localise and Count with Incomplete Dot-annotations (2021) (1)
Audio-Visual Predictive Coding for Self-Supervised Visual Representation Learning (2021) (1)
The effect of depth context in the segmentation of the colon in MRI volumes (2020) (1)
Erratum (1994) (0)
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 (2016) (0)
Erratum (2006) (0)
Identification of nitrogen-dependent QTL and underlying genes for root system architecture in hexaploid wheat (2019) (0)
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 (2017) (0)
AutoRoot: fully-automated plant root phenotyping (2016) (0)
Recovering Wind-induced Plant Motion in Dense Field Environments 3 via Deep Learning and Multiple Object Tracking 4 5 6 (2019) (0)
Quantification of fluorescent reporters in plant cells. (2015) (0)
Breakthrough Technologies RootNav : Navigating Images of Complex Root Architectures 1 [ C ] [ W ] (2013) (0)
AutoRoot: open-source software employing a novel image analysis approach to support fully-automated plant phenotyping (2017) (0)
Running head : Automated recovery of 3 D models of plant shoots Corresponding (2014) (0)
Pound, Michael P. and Atkinson, Jonathan A. and Wells, Darren M. and Pridmore, Tony P. and French, Andrew P. (2017) Deep learning for multi-task plant phenotyping (2017) (0)
Tracking and identifying groups of moving targets using probabilistic parsing (2010) (0)
CNN based Heuristic Function for A* Pathfinding Algorithm: Using Spatial Vector Data to Reconstruct Smooth and Natural Looking Plant Roots (2021) (0)
Handling Incomplete Instance Annotations via Asymmetric Loss Function (2021) (0)
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 (2017) (0)
Counting Pollen Viability via Deep Learning (2020) (0)
Addressing Multiple Salient Object Detection via Dual-Space Long-Range Dependencies (2021) (0)

This paper list is powered by the following services:

Other Resources About Michael Pound

en.wikipedia.org

What Schools Are Affiliated With Michael Pound?

Michael Pound is affiliated with the following schools:

University of Nottingham