What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Computer vision
His main research concerns Artificial intelligence, Algorithm, Pattern recognition, Optics and Computer vision.
His study brings together the fields of Machine learning and Artificial intelligence.
His Algorithm research is multidisciplinary, incorporating elements of Filter design, Noise, Signal-to-noise ratio, Noise and Minimum-variance unbiased estimator.
His Pattern recognition study combines topics in areas such as Facial recognition system, Key and Correlation.
His Optics research includes themes of Spectral density, Fourier transform, Matched filter and Pattern recognition.
His studies deal with areas such as Linear discriminant analysis and Principal component analysis as well as Computer vision.
His most cited work include:
- Minimum average correlation energy filters (566 citations)
- Performance measures for correlation filters. (444 citations)
- Tutorial survey of composite filter designs for optical correlators (360 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Algorithm, Pattern recognition, Computer vision and Optics.
His Biometrics, Facial recognition system, Feature extraction, Pattern recognition and Face investigations are all subjects of Artificial intelligence research.
The various areas that B. V. K. Vijaya Kumar examines in his Algorithm study include Noise, Communication channel and Binary number.
The study incorporates disciplines such as Machine learning, Object detection, Speech recognition and Correlation in addition to Pattern recognition.
His research on Computer vision frequently links to adjacent areas such as Probabilistic logic.
Within one scientific family, B. V. K. Vijaya Kumar focuses on topics pertaining to Matched filter under Optics, and may sometimes address concerns connected to Signal-to-noise ratio.
He most often published in these fields:
- Artificial intelligence (44.03%)
- Algorithm (28.12%)
- Pattern recognition (23.34%)
What were the highlights of his more recent work (between 2015-2020)?
- Artificial intelligence (44.03%)
- Pattern recognition (23.34%)
- Machine learning (5.57%)
In recent papers he was focusing on the following fields of study:
B. V. K. Vijaya Kumar focuses on Artificial intelligence, Pattern recognition, Machine learning, Facial recognition system and Algorithm.
B. V. K. Vijaya Kumar has researched Artificial intelligence in several fields, including Metric and Computer vision.
His Pattern recognition study integrates concerns from other disciplines, such as Margin, Inference and Biometrics.
His work in the fields of Machine learning, such as Latent variable, overlaps with other areas such as Specific knowledge.
His Facial recognition system research integrates issues from Feature extraction and Facial expression.
His Algorithm research is multidisciplinary, incorporating elements of Binary erasure channel, Deep learning, Bit error rate and Binary number.
Between 2015 and 2020, his most popular works were:
- Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-training (342 citations)
- One Network to Solve Them All — Solving Linear Inverse Problems Using Deep Projection Models (188 citations)
- Confidence Regularized Self-Training (113 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Computer vision
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Pattern recognition, Latent variable and Facial recognition system.
His study looks at the intersection of Artificial intelligence and topics like Metric with Function.
His Machine learning research is multidisciplinary, incorporating perspectives in Classifier, Heartbeat and Pattern matching.
His Pattern recognition study combines topics in areas such as Simple, Image, Ordinal regression and Ordinal data.
In his study, which falls under the umbrella issue of Latent variable, Regularization, Self training and Contextual image classification is strongly linked to Segmentation.
His research in Facial recognition system intersects with topics in Feature extraction, Discriminative model and Facial expression.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
