What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Thomas M. Breuel mainly investigates Artificial intelligence, Information retrieval, Optical character recognition, Pattern recognition and Computer vision.
His Artificial intelligence research incorporates themes from Machine learning and Natural language processing.
His Information retrieval research incorporates elements of Content-based image retrieval, Image retrieval and The Internet, World Wide Web, Upload.
The various areas that Thomas M. Breuel examines in his Optical character recognition study include Window, Document processing, Speech recognition and Pattern recognition.
His Computer vision study incorporates themes from Perspective, Distortion, Adaptive algorithm and Curl.
In general Image, his work in Image translation is often linked to Joint probability distribution linking many areas of study.
His most cited work include:
- Unsupervised Image-to-Image Translation Networks (833 citations)
- Large-scale visual sentiment ontology and detectors using adjective noun pairs (472 citations)
- Unsupervised Image-to-Image Translation Networks (456 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Pattern recognition, Computer vision, Optical character recognition and Information retrieval.
His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning, Speech recognition and Natural language processing.
His Pattern recognition study combines topics in areas such as Feature and Spotting.
His Computer vision research is multidisciplinary, relying on both Perspective and Robustness.
Thomas M. Breuel interconnects Handwriting recognition, Document processing and Pattern recognition in the investigation of issues within Optical character recognition.
His biological study deals with issues like Data mining, which deal with fields such as Line.
He most often published in these fields:
- Artificial intelligence (64.07%)
- Pattern recognition (28.14%)
- Computer vision (25.11%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (64.07%)
- Recurrent neural network (7.36%)
- Speech recognition (13.42%)
In recent papers he was focusing on the following fields of study:
Thomas M. Breuel focuses on Artificial intelligence, Recurrent neural network, Speech recognition, Machine learning and Artificial neural network.
His studies deal with areas such as Natural language processing and Pattern recognition as well as Artificial intelligence.
The Pattern recognition study combines topics in areas such as Feature, Face and Benchmark.
His research integrates issues of Character, Normalization, Optical character recognition and Scripting language in his study of Speech recognition.
Thomas M. Breuel has researched Optical character recognition in several fields, including Handwriting recognition and Segmentation.
His Image research is multidisciplinary, incorporating elements of Infinite set and Translation.
Between 2013 and 2021, his most popular works were:
- Unsupervised Image-to-Image Translation Networks (833 citations)
- Unsupervised Image-to-Image Translation Networks (456 citations)
- Scene labeling with LSTM recurrent neural networks (284 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
His primary areas of study are Artificial intelligence, Artificial neural network, Speech recognition, Recurrent neural network and Machine learning.
His Artificial intelligence research integrates issues from Margin and Pattern recognition.
His work deals with themes such as RGB color model, Image processing and Benchmark, which intersect with Pattern recognition.
Thomas M. Breuel combines subjects such as Layer, Optical character recognition, Natural language processing, Decoding methods and Gesture with his study of Speech recognition.
The study incorporates disciplines such as Segmentation, Image segmentation, Connectionism and Scripting language in addition to Recurrent neural network.
His Image translation study in the realm of Image connects with subjects such as Joint probability distribution.
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