What is he best known for?
The fields of study David P. Casasent is best known for:
- Fourier transform
- Optics
- Radar
In the subject of Artificial intelligence, David P. Casasent integrates adjacent academic fields such as Pattern recognition (psychology), Image (mathematics), Artificial neural network and Feature extraction.
His research on Image (mathematics) frequently connects to adjacent areas such as Computer vision.
His Computer vision study frequently involves adjacent topics like Filter (signal processing).
David P. Casasent performs multidisciplinary study in the fields of Optics and Spatial filter via his papers.
Many of his studies on Telecommunications involve topics that are commonly interrelated, such as Clutter.
By researching both Clutter and Radar, he produces research that crosses academic boundaries.
He combines topics linked to Telecommunications with his work on Radar.
He integrates Algorithm and Artificial intelligence in his studies.
His research on Geometry frequently links to adjacent areas such as Correlation.
His most cited work include:
- Minimum average correlation energy filters (563 citations)
- Position, rotation, and scale invariant optical correlation (492 citations)
- Multivariant technique for multiclass pattern recognition (362 citations)
What are the main themes of his work throughout his whole career to date
David P. Casasent connects Optics with Quantum mechanics in his study.
He integrates several fields in his works, including Quantum mechanics and Optics.
David P. Casasent brings together Artificial intelligence and Algorithm to produce work in his papers.
He integrates Algorithm with Artificial intelligence in his research.
As part of his studies on Computer vision, David P. Casasent frequently links adjacent subjects like Filter (signal processing).
His work in Filter (signal processing) is not limited to one particular discipline; it also encompasses Computer vision.
His research on Telecommunications frequently connects to adjacent areas such as Radar.
His study brings together the fields of Telecommunications and Radar.
Image (mathematics) and Image processing are commonly linked in his work.
David P. Casasent most often published in these fields:
- Optics (62.03%)
- Artificial intelligence (59.36%)
- Computer vision (40.11%)
What were the highlights of his more recent work (between 1997-2009)?
- Artificial intelligence (82.61%)
- Pattern recognition (psychology) (65.22%)
- Computer vision (56.52%)
In recent works David P. Casasent was focusing on the following fields of study:
Many of his studies involve connections with topics such as Product (mathematics), Quadratic equation and Piecewise linear function and Geometry.
His Optics studies intersect with other subjects such as Optoelectronics and Diffraction.
He performs integrative study on Optoelectronics and Optics in his works.
His research on Artificial intelligence often connects related topics like Classifier (UML).
His study connects Object detection and Pattern recognition (psychology).
His research on Object detection often connects related areas such as Pattern recognition (psychology).
His study on Computer vision is mostly dedicated to connecting different topics, such as Filter (signal processing).
The study of Filter (signal processing) is intertwined with the study of Computer vision in a number of ways.
David P. Casasent performs integrative study on Algorithm and Artificial intelligence.
Between 1997 and 2009, his most popular works were:
- An improvement on floating search algorithms for feature subset selection (113 citations)
- Radial basis function neural networks for nonlinear Fisher discrimination and Neyman–Pearson classification (70 citations)
- Adaptive branch and bound algorithm for selecting optimal features (50 citations)
In his most recent research, the most cited works focused on:
- Machine learning
- Support vector machine
- Artificial neural network
His study ties his expertise on Selection (genetic algorithm) together with the subject of Artificial intelligence.
Selection (genetic algorithm) is often connected to Artificial intelligence in his work.
His study connects Facial recognition system and Pattern recognition (psychology).
His work on Facial recognition system is being expanded to include thematically relevant topics such as Kernel Fisher discriminant analysis.
As part of his studies on Kernel Fisher discriminant analysis, he often connects relevant areas like Fisher kernel.
Much of his study explores Fisher kernel relationship to Pattern recognition (psychology).
In most of his Algorithm studies, his work intersects topics such as Branch and bound.
His research on Branch and bound often connects related areas such as Algorithm.
His Geometry study often links to related topics such as Piecewise linear function.
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