What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Digital watermarking and Image processing.
He combines topics linked to Algorithm with his work on Artificial intelligence.
His biological study spans a wide range of topics, including Matrix decomposition and Facial expression.
His Digital watermarking research is multidisciplinary, incorporating elements of Transform coding and Computer security.
His study on Image processing also encompasses disciplines like
- Adaptive filter which is related to area like Digital filter,
- Signal processing which intersects with area such as Median filter.
In his study, which falls under the umbrella issue of Feature extraction, Three-dimensional face recognition is strongly linked to Face hallucination.
His most cited work include:
- Nonlinear Digital Filters (977 citations)
- Nonlinear Digital Filters: Principles and Applications (908 citations)
- Facial Expression Recognition in Image Sequences Using Geometric Deformation Features and Support Vector Machines (541 citations)
What are the main themes of his work throughout his whole career to date?
Ioannis Pitas mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Facial recognition system.
His Artificial intelligence study frequently links to other fields, such as Machine learning.
Digital watermarking, Video tracking, Face, Image segmentation and Watermark are among the areas of Computer vision where the researcher is concentrating his efforts.
He interconnects Chaotic, Theoretical computer science and Robustness in the investigation of issues within Digital watermarking.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Contextual image classification, Facial expression and Cluster analysis.
His Algorithm research incorporates themes from Digital filter, Filter and Signal processing.
He most often published in these fields:
- Artificial intelligence (70.52%)
- Computer vision (41.77%)
- Pattern recognition (38.08%)
What were the highlights of his more recent work (between 2012-2021)?
- Artificial intelligence (70.52%)
- Pattern recognition (38.08%)
- Computer vision (41.77%)
In recent papers he was focusing on the following fields of study:
Ioannis Pitas focuses on Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Cluster analysis.
His research in Facial recognition system, Extreme learning machine, Video tracking, Support vector machine and Stereoscopy are components of Artificial intelligence.
Ioannis Pitas studied Pattern recognition and Subspace topology that intersect with Graph embedding and Projection.
His studies examine the connections between Computer vision and genetics, as well as such issues in Visualization, with regards to Feature extraction.
His Machine learning research includes themes of Representation and Training set.
He combines subjects such as Segmentation and Automatic summarization with his study of Cluster analysis.
Between 2012 and 2021, his most popular works were:
- On the kernel Extreme Learning Machine classifier (95 citations)
- Minimum Class Variance Extreme Learning Machine for Human Action Recognition (95 citations)
- Graph Embedded Extreme Learning Machine (82 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, Machine learning, Computer vision and Extreme learning machine.
His Feature vector, Training set, Video tracking, Representation and Cluster analysis investigations are all subjects of Artificial intelligence research.
His work on Subspace topology expands to the thematically related Pattern recognition.
Ioannis Pitas works mostly in the field of Machine learning, limiting it down to topics relating to Process and, in certain cases, Classifier, Sigmoid function and Hyperplane, as a part of the same area of interest.
His Computer vision study often links to related topics such as Cinematography.
His research in Extreme learning machine intersects with topics in Contextual image classification, Regularization and Feedforward neural network.
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