What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Object detection and Contextual image classification.
His Estimation theory research extends to Artificial intelligence, which is thematically connected.
His study in the field of Scale-invariant feature transform, Color image and Ground truth also crosses realms of Detector.
His Pattern recognition study combines topics from a wide range of disciplines, such as Supervised learning and Outlier.
The concepts of his Object detection study are interwoven with issues in Artificial neural network, Statistical classification, RANSAC and Robustness.
His work carried out in the field of Contextual image classification brings together such families of science as Feature extraction, Support vector machine and Computer experiment.
His most cited work include:
- Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab (302 citations)
- GANomaly : semi-supervised anomaly detection via adversarial training. (201 citations)
- Real-Time Monocular Depth Estimation Using Synthetic Data with Domain Adaptation via Image Style Transfer (146 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Convolutional neural network and Object detection.
Artificial intelligence is frequently linked to Machine learning in his study.
His Feature, Monocular, RGB color model and Image segmentation study in the realm of Computer vision connects with subjects such as Context.
He combines topics linked to Contextual image classification with his work on Pattern recognition.
His Convolutional neural network study integrates concerns from other disciplines, such as Transfer of learning, Pixel and Range.
His Object detection course of study focuses on Cognitive neuroscience of visual object recognition and Scale-invariant feature transform.
He most often published in these fields:
- Artificial intelligence (85.99%)
- Computer vision (46.38%)
- Pattern recognition (31.88%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (85.99%)
- Computer vision (46.38%)
- Pattern recognition (31.88%)
In recent papers he was focusing on the following fields of study:
Toby P. Breckon mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Convolutional neural network and Object detection.
In his research, Classifier is intimately related to Machine learning, which falls under the overarching field of Artificial intelligence.
His work deals with themes such as Multi-task learning and X ray computed, which intersect with Computer vision.
His study looks at the relationship between Pattern recognition and fields such as Image translation, as well as how they intersect with chemical problems.
His biological study spans a wide range of topics, including Transformation and Pixel.
His Object detection study also includes fields such as
- Computed tomography and related False positive rate, False alarm, Support vector machine and Airport security,
- JPEG which intersects with area such as Transform coding, Pose, Quantization and Image segmentation.
Between 2019 and 2021, his most popular works were:
- Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling (25 citations)
- Towards Automatic Threat Detection: A Survey of Advances of Deep Learning within X-ray Security Imaging. (12 citations)
- An approach for adaptive automatic threat recognition within 3D computed tomography images for baggage security screening. (11 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Artificial intelligence, Pattern recognition, Convolutional neural network, Feature vector and Object detection are his primary areas of study.
His research integrates issues of Machine learning and Computer vision in his study of Artificial intelligence.
The Computer vision study combines topics in areas such as Metal artefact and Quantitative Evaluations.
Toby P. Breckon has researched Pattern recognition in several fields, including Transfer of learning, Subspace topology and Computed tomography.
His Convolutional neural network study combines topics in areas such as Object and Multilayer perceptron.
His Object detection research integrates issues from Tracking and False positive rate.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
