What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
Josef Kittler focuses on Artificial intelligence, Pattern recognition, Computer vision, Algorithm and Machine learning.
His work on Artificial intelligence deals in particular with Image processing, Pattern recognition, Facial recognition system, Image and Pixel.
His Pattern recognition study frequently draws connections between adjacent fields such as Feature.
His studies in Algorithm integrate themes in fields like Probabilistic logic, Edge detection and Relaxation.
The study incorporates disciplines such as Contextual image classification and Feature in addition to Feature extraction.
Josef Kittler combines subjects such as Decision theory and Fusion with his study of Classifier.
His most cited work include:
- On combining classifiers (4702 citations)
- Floating search methods in feature selection (2473 citations)
- Pattern recognition : a statistical approach (2392 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Computer vision, Facial recognition system and Machine learning.
His study in Face, Feature extraction, Image, Image processing and Classifier falls under the purview of Artificial intelligence.
He regularly links together related areas like Feature in his Pattern recognition studies.
His research integrates issues of Invariant and Biometrics in his study of Facial recognition system.
His biological study spans a wide range of topics, including Speech recognition and Data mining.
His Machine learning study typically links adjacent topics like Context.
He most often published in these fields:
- Artificial intelligence (78.18%)
- Pattern recognition (43.42%)
- Computer vision (31.83%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (78.18%)
- Pattern recognition (43.42%)
- Computer vision (31.83%)
In recent papers he was focusing on the following fields of study:
Josef Kittler spends much of his time researching Artificial intelligence, Pattern recognition, Computer vision, Discriminative model and Facial recognition system.
His work investigates the relationship between Artificial intelligence and topics such as Machine learning that intersect with problems in Key.
The Pattern recognition study combines topics in areas such as Subspace topology, Focus, Feature and Regularization.
His Discriminative model study combines topics in areas such as Video tracking, Matrix, Classifier, Contextual image classification and Least squares.
The concepts of his Video tracking study are interwoven with issues in Artificial neural network, Filter, Feature selection and Source code.
His studies deal with areas such as Margin, Invariant and Dictionary learning as well as Facial recognition system.
Between 2015 and 2021, his most popular works were:
- The sixth visual object tracking VOT2018 challenge results (299 citations)
- Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks (163 citations)
- A Multiresolution 3D Morphable Face Model and Fitting Framework (134 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
His primary areas of study are Artificial intelligence, Computer vision, Pattern recognition, Facial recognition system and Face.
His research related to Robustness, Feature extraction, Video tracking, Landmark and Discriminative model might be considered part of Artificial intelligence.
Josef Kittler usually deals with Computer vision and limits it to topics linked to Invariant and Covariance, Model selection and Mixture model.
His work on Training set as part of general Pattern recognition study is frequently connected to Minority class, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Facial recognition system research includes elements of Representation, Matrix, Deep learning and Regression.
His Face research also works with subjects such as
- Anomaly detection and related Attack model, Protocol, Face Presentation and Outlier,
- Spoofing attack which connect with Class, Anomaly, Component and Machine learning,
- Identification which is related to area like Genetics and Heritability.
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