What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- The Internet
Ioannis Kompatsiaris focuses on Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Image segmentation.
His study connects Machine learning and Artificial intelligence.
Ioannis Kompatsiaris combines subjects such as Exploit, Initialization, Convolutional neural network and Vascular disease with his study of Computer vision.
His work on Optimal discriminant analysis, Discriminant and Linear discriminant analysis is typically connected to Metric as part of general Pattern recognition study, connecting several disciplines of science.
His Segmentation research integrates issues from Object detection, Coronary arteries, Radiology and Visualization.
His research in Feature extraction intersects with topics in Histogram and Robustness.
His most cited work include:
- Sensing Trending Topics in Twitter (295 citations)
- Real-time compressed-domain spatiotemporal segmentation and ontologies for video indexing and retrieval (158 citations)
- Deep Learning Advances in Computer Vision with 3D Data: A Survey (144 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Information retrieval, Computer vision, Pattern recognition and Multimedia.
His research links Machine learning with Artificial intelligence.
The concepts of his Information retrieval study are interwoven with issues in Domain, Data mining and Image retrieval.
His study in Computer vision focuses on Video tracking, Object detection, Motion compensation, Image processing and Block-matching algorithm.
Much of his study explores Multimedia relationship to World Wide Web.
He works on Image segmentation which deals in particular with Scale-space segmentation.
He most often published in these fields:
- Artificial intelligence (39.05%)
- Information retrieval (17.16%)
- Computer vision (16.29%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (39.05%)
- Pattern recognition (15.59%)
- Social media (6.83%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Pattern recognition, Social media, Human–computer interaction and Ontology.
His studies in Artificial intelligence integrate themes in fields like Machine learning, Task and Computer vision.
When carried out as part of a general Computer vision research project, his work on Object detection is frequently linked to work in Drone and Trajectory, therefore connecting diverse disciplines of study.
His study in Feature extraction and Discriminative model is done as part of Pattern recognition.
As a part of the same scientific family, Ioannis Kompatsiaris mostly works in the field of Social media, focusing on Data science and, on occasion, Domain.
His biological study spans a wide range of topics, including Representation and World Wide Web.
Between 2017 and 2021, his most popular works were:
- EEG-Based Brain-Computer Interfaces for Communication and Rehabilitation of People with Motor Impairment: A Novel Approach of the 21 st Century. (87 citations)
- Bias in data-driven artificial intelligence systems - An introductory survey (29 citations)
- Oil Spill Identification from Satellite Images Using Deep Neural Networks (27 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
Ioannis Kompatsiaris mostly deals with Artificial intelligence, Pattern recognition, Brain–computer interface, Benchmark and Machine learning.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Field, State and Computer vision.
His Discriminative model and Feature extraction study, which is part of a larger body of work in Pattern recognition, is frequently linked to Brainwave entrainment, bridging the gap between disciplines.
In his study, which falls under the umbrella issue of Brain–computer interface, Support vector machine is strongly linked to Decoding methods.
His Benchmark study combines topics from a wide range of disciplines, such as Object, Segmentation and Artificial neural network.
His Machine learning study integrates concerns from other disciplines, such as Classifier and Decision level.
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