His primary scientific interests are in Artificial intelligence, Speech recognition, Hidden Markov model, Pattern recognition and Computer vision. Gernot A. Fink regularly links together related areas like Natural language processing in his Artificial intelligence studies. His Speech recognition study incorporates themes from Feature and Mel-frequency cepstrum.
As a part of the same scientific study, he usually deals with the Hidden Markov model, concentrating on Markov model and frequently concerns with Pattern recognition. His study on Discriminative model is often connected to Retinopathy and Diabetic retinopathy as part of broader study in Pattern recognition. His Computer vision research is multidisciplinary, relying on both Human–robot interaction and Mobile robot.
Gernot A. Fink spends much of his time researching Artificial intelligence, Speech recognition, Natural language processing, Pattern recognition and Hidden Markov model. His studies deal with areas such as Machine learning and Computer vision as well as Artificial intelligence. His Computer vision research integrates issues from Robot and Mobile robot.
His study in the field of Semantic network is also linked to topics like Task and Intelligent word recognition. His Pattern recognition study combines topics from a wide range of disciplines, such as Contextual image classification, Probabilistic logic and Image retrieval. His Hidden Markov model study integrates concerns from other disciplines, such as Context, Training set, Set, Markov model and Arabic.
The scientist’s investigation covers issues in Artificial intelligence, Spotting, Word, Pattern recognition and Natural language processing. His Artificial intelligence study combines topics in areas such as Field and Machine learning. His Spotting research includes elements of Vocabulary and Benchmark.
He has included themes like Embedding, Probabilistic logic, String and Contrast in his Word study. Gernot A. Fink interconnects Contextual image classification, Ground truth and Feature in the investigation of issues within Pattern recognition. His research integrates issues of Annotation, Speech recognition, Hidden Markov model and Training set in his study of Natural language processing.
Gernot A. Fink mainly focuses on Artificial intelligence, Spotting, Word, Convolutional neural network and Pattern recognition. His research in Artificial intelligence intersects with topics in Field, Speech recognition and Natural language processing. His Natural language processing research incorporates themes from Transcription and Hidden Markov model.
His work investigates the relationship between Spotting and topics such as Segmentation that intersect with problems in Documentation and Computer-aided. His work is dedicated to discovering how Word, Representation are connected with State and other disciplines. While the research belongs to areas of Pattern recognition, he spends his time largely on the problem of Contextual image classification, intersecting his research to questions surrounding Discriminative model.
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.
Markov Models for Pattern Recognition: From Theory to Applications
Gernot A. Fink.
Markov models for offline handwriting recognition: a survey
Thomas Plötz;Gernot A. Fink.
International Journal on Document Analysis and Recognition (2009)
Combining acoustic and articulatory feature information for robust speech recognition
Katrin Kirchhoff;Gernot A Fink;Gerhard Sagerer.
Speech Communication (2002)
Providing the basis for human-robot-interaction: a multi-modal attention system for a mobile robot
Sebastian Lang;Marcus Kleinehagenbrock;Sascha Hohenner;Jannik Fritsch.
international conference on multimodal interfaces (2003)
PHOCNet: A Deep Convolutional Neural Network for Word Spotting in Handwritten Documents
Sebastian Sudholt;Gernot A. Fink.
international conference on frontiers in handwriting recognition (2016)
BIRON - The Bielefeld Robot Companion
Axel Haasch;Sascha Hohenner;Sonja Hüwel;Marcus Kleinehagenbrock.
Proc. Int. Workshop on Advances in Service Robotics (2004)
Multi-modal anchoring for human–robot interaction
Jannik Fritsch;Marcus Kleinehagenbrock;Sebastian Lang;Thomas Plötz.
Robotics and Autonomous Systems (2003)
Developing HMM-Based Recognizers with ESMERALDA
Gernot A. Fink.
text speech and dialogue (1999)
Multi-modal human-machine communication for instructing robot grasping tasks
P. McGuire;J. Fritsch;J.J. Steil;F. Rothling.
intelligent robots and systems (2002)
Person tracking with a mobile robot based on multi-modal anchoring
M. Kleinehagenbrock;S. Lang;J. Fritsch;F. Lomker.
robot and human interactive communication (2002)
Profile was last updated on December 6th, 2021.
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