What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
George Bebis focuses on Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Object detection.
His study ties his expertise on Machine learning together with the subject of Artificial intelligence.
His Computer vision study deals with Robustness intersecting with Contextual image classification, Edge detection, Advanced driver assistance systems and Artificial neural network.
As a part of the same scientific study, George Bebis usually deals with the Pattern recognition, concentrating on Thresholding and frequently concerns with Video tracking and Tracking system.
His research in Feature extraction intersects with topics in Feature, Visual inspection, Face detection, Minutiae and Search engine indexing.
George Bebis has included themes like Remotely operated underwater vehicle, Mobile robot, Background subtraction, Real-time computing and Machine vision in his Object detection study.
His most cited work include:
- On-road vehicle detection: a review (934 citations)
- Vision-based hand pose estimation: A review (682 citations)
- Object detection using feature subset selection (227 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Support vector machine.
George Bebis combines topics linked to Machine learning with his work on Artificial intelligence.
His work in Computer vision is not limited to one particular discipline; it also encompasses Robustness.
His study looks at the intersection of Pattern recognition and topics like Local binary patterns with Feature vector.
The study incorporates disciplines such as Contextual image classification and Feature in addition to Feature extraction.
His research investigates the link between Support vector machine and topics such as Chrominance that cross with problems in Digital image.
He most often published in these fields:
- Artificial intelligence (80.31%)
- Computer vision (50.58%)
- Pattern recognition (40.15%)
What were the highlights of his more recent work (between 2012-2021)?
- Artificial intelligence (80.31%)
- Pattern recognition (40.15%)
- Computer vision (50.58%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Computer vision, Support vector machine and Local binary patterns.
As part of his studies on Artificial intelligence, George Bebis frequently links adjacent subjects like Machine learning.
His Pattern recognition research incorporates elements of Histogram and Feature.
His study connects Cluster analysis and Computer vision.
His work in Support vector machine covers topics such as Pixel which are related to areas like Horizon line.
His Local binary patterns study integrates concerns from other disciplines, such as Image texture, Wavelet, Discrete wavelet transform and Discrete cosine transform.
Between 2012 and 2021, his most popular works were:
- Review: A survey of network flow applications (143 citations)
- Image forgery detection using steerable pyramid transform and local binary pattern (113 citations)
- A supervised machine learning approach to classify host roles on line using sFlow (71 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary scientific interests are in Artificial intelligence, Pattern recognition, Support vector machine, Computer vision and Chrominance.
His research on Artificial intelligence frequently connects to adjacent areas such as Machine learning.
His biological study spans a wide range of topics, including Template matching and Biometrics.
His work on Discriminative model is typically connected to Invariant as part of general Pattern recognition study, connecting several disciplines of science.
His Chrominance research is multidisciplinary, relying on both Image and Digital image.
His Feature extraction research is multidisciplinary, incorporating perspectives in Classifier and Feature engineering.
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