What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His primary scientific interests are in Artificial intelligence, Speech recognition, Pattern recognition, Computer vision and Hidden Markov model.
His Artificial intelligence study frequently links to related topics such as Machine learning.
The various areas that Gerhard Rigoll examines in his Speech recognition study include Recurrent neural network, Support vector machine and Natural language processing.
His Pattern recognition study integrates concerns from other disciplines, such as Face and Sensor fusion.
His research integrates issues of Gait and Convolutional neural network in his study of Computer vision.
Gerhard Rigoll has researched Hidden Markov model in several fields, including Signature recognition, Projection, Decoding methods, Vocabulary and Markov model.
His most cited work include:
- Hidden Markov model-based speech emotion recognition (446 citations)
- Background segmentation with feedback: The Pixel-Based Adaptive Segmenter (435 citations)
- SVC2004: First International Signature Verification Competition (349 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Speech recognition, Pattern recognition, Hidden Markov model and Computer vision.
Gerhard Rigoll combines subjects such as Machine learning and Natural language processing with his study of Artificial intelligence.
His studies deal with areas such as Recurrent neural network, Feature, Vocabulary and Support vector machine as well as Speech recognition.
The Pattern recognition study combines topics in areas such as Facial recognition system and Feature.
His Hidden Markov model research incorporates elements of Cursive, Signature recognition, Handwriting recognition, Markov model and Pattern recognition.
His Gesture research is multidisciplinary, incorporating perspectives in Convolutional neural network and Human–computer interaction.
He most often published in these fields:
- Artificial intelligence (64.69%)
- Speech recognition (41.22%)
- Pattern recognition (28.99%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (64.69%)
- Convolutional neural network (4.93%)
- Pattern recognition (28.99%)
In recent papers he was focusing on the following fields of study:
Gerhard Rigoll mostly deals with Artificial intelligence, Convolutional neural network, Pattern recognition, Computer vision and Speech recognition.
His Artificial intelligence research includes themes of Gait and Machine learning.
His work carried out in the field of Convolutional neural network brings together such families of science as Network architecture, Kernel, Gesture and RGB color model.
The concepts of his Speech recognition study are interwoven with issues in End-to-end principle, Artificial neural network and Test set.
His Feature extraction research integrates issues from Facial recognition system and Feature.
His Hidden Markov model study combines topics in areas such as Training set and German.
Between 2016 and 2021, his most popular works were:
- A deep convolutional neural network for video sequence background subtraction (184 citations)
- Robust Facial Landmark Detection via a Fully-Convolutional Local-Global Context Network (50 citations)
- A Deep Convolutional Neural Network for Background Subtraction. (48 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His main research concerns Artificial intelligence, Convolutional neural network, Pattern recognition, Computer vision and Kernel.
His research on Artificial intelligence frequently connects to adjacent areas such as Code.
His Convolutional neural network research is multidisciplinary, relying on both Network architecture and Distraction.
He interconnects Gait, Video tracking and Biometrics in the investigation of issues within Pattern recognition.
His Computer vision research incorporates themes from Task, Activity recognition and Key frame.
His Kernel research is multidisciplinary, incorporating elements of Artificial neural network, Outlier, Statistical model, Kernel and Convolution.
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