What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of investigation include Botany, Artificial intelligence, Arabidopsis, Cell biology and Auxin.
His work on Root system as part of general Botany study is frequently linked to Process, bridging the gap between disciplines.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning, Computer vision and Pattern recognition.
His work in the fields of Machine learning, such as Feature, intersects with other areas such as Overall survival and Glioma.
In the subject of general Arabidopsis, his work in Root gravitropism is often linked to Stimulus, Root growth and Biophysics, thereby combining diverse domains of study.
His work carried out in the field of Root brings together such families of science as Range, Pixel and Component.
His most cited work include:
- Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge (493 citations)
- The Visual Object Tracking VOT2016 Challenge Results (462 citations)
- Developing X-ray Computed Tomography to non-invasively image 3-D root systems architecture in soil (262 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Segmentation, Pattern recognition and Root.
Tony P. Pridmore focuses mostly in the field of Artificial intelligence, narrowing it down to matters related to Machine learning and, in some cases, Plant phenotyping.
His Computer vision research focuses on subjects like Robotic arm, which are linked to Depth map, Cognitive neuroscience of visual object recognition, Object model, Orientation and Robotics.
His Pattern recognition research extends to Segmentation, which is thematically connected.
His Root research incorporates elements of Biological system and Plant roots.
His research in Particle filter intersects with topics in Eye tracking and Markov chain Monte Carlo.
He most often published in these fields:
- Artificial intelligence (52.02%)
- Computer vision (27.78%)
- Segmentation (12.63%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (52.02%)
- Segmentation (12.63%)
- Pattern recognition (11.62%)
In recent papers he was focusing on the following fields of study:
Tony P. Pridmore focuses on Artificial intelligence, Segmentation, Pattern recognition, Deep learning and Convolutional neural network.
His Artificial intelligence study combines topics in areas such as Machine learning, Key and Computer vision.
His Machine learning study incorporates themes from Image based, Brain tumor segmentation and Algorithm.
His research in Segmentation focuses on subjects like Pattern recognition, which are connected to Identification, Noise reduction and Code.
His studies in Pattern recognition integrate themes in fields like Context, Modality and Root.
His Deep learning research is multidisciplinary, incorporating elements of Upsampling, Pixel, Translation and Dice.
Between 2017 and 2021, his most popular works were:
- Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge (493 citations)
- Rice auxin influx carrier OsAUX1 facilitates root hair elongation in response to low external phosphate (49 citations)
- Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge (41 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Artificial intelligence, Segmentation, Deep learning, Convolutional neural network and Pattern recognition are his primary areas of study.
Tony P. Pridmore has researched Artificial intelligence in several fields, including Machine learning and Computer vision.
His work in Machine learning addresses issues such as Algorithm, which are connected to fields such as Accurate segmentation.
His research integrates issues of Plant morphology and Position in his study of Computer vision.
His Convolutional neural network study combines topics from a wide range of disciplines, such as Baseline and Overfitting.
The study incorporates disciplines such as Artificial neural network, Cluster analysis, Pixel and Root in addition to Pattern recognition.
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