His primary scientific interests are in Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Feature extraction. While working in this field, he studies both Artificial intelligence and Focus. His Pattern recognition study integrates concerns from other disciplines, such as Facial recognition system and Feature.
His Computer vision research incorporates themes from Painting and Position. In the field of Machine learning, his study on Active learning overlaps with subjects such as Fine-tuning. The Feature extraction study combines topics in areas such as Contextual image classification, Semantic similarity, Semantics, Random forest and Discriminative model.
Joachim Denzler focuses on Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Cognitive neuroscience of visual object recognition. He brings together Artificial intelligence and Field to produce work in his papers. His Computer vision and Tracking, Video tracking, Object detection, Active appearance model and 3D reconstruction investigations all form part of his Computer vision research activities.
His work on Segmentation, Feature extraction, Support vector machine and Classifier as part of general Pattern recognition research is often related to Gaussian process, thus linking different fields of science. His Categorization research extends to the thematically linked field of Machine learning. His Cognitive neuroscience of visual object recognition study frequently links to other fields, such as Context.
Joachim Denzler mainly investigates Artificial intelligence, Machine learning, Deep learning, Pattern recognition and Object detection. As a part of the same scientific study, Joachim Denzler usually deals with the Artificial intelligence, concentrating on Computer vision and frequently concerns with Representation. Joachim Denzler combines subjects such as Training set and Domain knowledge with his study of Machine learning.
His study looks at the relationship between Deep learning and topics such as Terrain, which overlap with Global change, Generative model and Vegetation. Joachim Denzler has researched Pattern recognition in several fields, including Feature and Data set. Joachim Denzler works mostly in the field of Object detection, limiting it down to topics relating to Pascal and, in certain cases, Incremental learning, Context, Active learning and Leverage, as a part of the same area of interest.
His primary areas of study are Artificial intelligence, Pattern recognition, Machine learning, Deep learning and Object detection. Image, Image retrieval, Semantics, Semantic similarity and Similarity are the primary areas of interest in his Artificial intelligence study. His Pattern recognition research integrates issues from Normalization, Cognitive neuroscience of visual object recognition and Pooling.
The Machine learning study which covers Training set that intersects with Test. His work on Softmax function as part of general Deep learning study is frequently connected to Process modeling, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His Computer vision research includes themes of Boosting and Amodal perception, Perception.
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Deep learning and process understanding for data-driven Earth system science
Markus Reichstein;Gustau Camps-Valls;Bjorn Stevens;Martin Jung.
Information theoretic sensor data selection for active object recognition and state estimation
J. Denzler;C.M. Brown.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2002)
Model based extraction of articulated objects in image sequences for gait analysis
D. Meyer;J. Denzler;H. Niemann.
international conference on image processing (1997)
One-class classification with gaussian processes
Michael Kemmler;Erik Rodner;Joachim Denzler.
asian conference on computer vision (2010)
Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning.
Marc Aubreville;Christian Knipfer;Christian Knipfer;Nicolai Oetter;Christian Jaremenko.
Scientific Reports (2017)
Fractal-Like Image Statistics in Visual Art: Similarity to Natural Scenes
Christoph Redies;Jens Hasenstein;Joachim Denzler.
Spatial Vision (2007)
Plenoptic Modeling and Rendering from Image Sequences Taken by Hand-Held Camera
Benno Heigl;Reinhard Koch;Marc Pollefeys;Joachim Denzler.
Mustererkennung 1999, 21. DAGM-Symposium (1999)
Selecting Influential Examples: Active Learning with Expected Model Output Changes
Alexander Freytag;Erik Rodner;Joachim Denzler.
european conference on computer vision (2014)
One-class classification with Gaussian processes
Michael Kemmler;Erik Rodner;Esther-Sabrina Wacker;Joachim Denzler.
Pattern Recognition (2013)
Nonparametric Part Transfer for Fine-Grained Recognition
Christoph Göering;Erik Rodner;Alexander Freytag;Joachim Denzler.
computer vision and pattern recognition (2014)
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