What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Nasser M. Nasrabadi spends much of his time researching Artificial intelligence, Pattern recognition, Sparse approximation, Hyperspectral imaging and Computer vision.
Facial recognition system, Feature vector, K-SVD, Kernel method and Feature are the primary areas of interest in his Artificial intelligence study.
His Pattern recognition study combines topics in areas such as Contextual image classification and Machine learning, Kernel.
His study looks at the relationship between Sparse approximation and topics such as Support vector machine, which overlap with Linear combination.
His research integrates issues of Subspace topology, Pixel, Object detection, Regularization and Matched filter in his study of Hyperspectral imaging.
His work carried out in the field of Computer vision brings together such families of science as Artificial neural network, Hopfield network and Anomaly detection.
His most cited work include:
- Image coding using vector quantization: a review (957 citations)
- Hyperspectral Remote Sensing Data Analysis and Future Challenges (952 citations)
- Hyperspectral Image Classification Using Dictionary-Based Sparse Representation (824 citations)
What are the main themes of his work throughout his whole career to date?
Nasser M. Nasrabadi mainly investigates Artificial intelligence, Pattern recognition, Computer vision, Artificial neural network and Vector quantization.
As a part of the same scientific study, Nasser M. Nasrabadi usually deals with the Artificial intelligence, concentrating on Machine learning and frequently concerns with Biometrics.
His work in Pattern recognition is not limited to one particular discipline; it also encompasses Contextual image classification.
His Computer vision research includes elements of Principal component analysis and Detector.
His Vector quantization research is multidisciplinary, incorporating perspectives in Codebook and Quantization.
His Hyperspectral imaging study combines topics in areas such as Subspace topology, Pixel, Anomaly detection, Object detection and Matched filter.
He most often published in these fields:
- Artificial intelligence (84.02%)
- Pattern recognition (55.81%)
- Computer vision (25.93%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (84.02%)
- Pattern recognition (55.81%)
- Face (7.68%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Pattern recognition, Face, Convolutional neural network and Facial recognition system.
Nasser M. Nasrabadi interconnects Machine learning and Computer vision in the investigation of issues within Artificial intelligence.
Particularly relevant to Feature extraction is his body of work in Pattern recognition.
His biological study spans a wide range of topics, including Sketch, Modality, Landmark and Similarity.
His research integrates issues of Feature, Classifier, Reduction, Contextual image classification and Automatic target recognition in his study of Convolutional neural network.
His study focuses on the intersection of Facial recognition system and fields such as Feature vector with connections in the field of Leverage.
Between 2017 and 2021, his most popular works were:
- Multi-Level Feature Abstraction from Convolutional Neural Networks for Multimodal Biometric Identification (33 citations)
- A Deep Face Identification Network Enhanced by Facial Attributes Prediction (33 citations)
- Deep Cross Polarimetric Thermal-to-Visible Face Recognition (32 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Nasser M. Nasrabadi mainly focuses on Artificial intelligence, Pattern recognition, Convolutional neural network, Feature extraction and Face.
Artificial neural network, Discriminative model, Facial recognition system, Deep learning and Image are among the areas of Artificial intelligence where the researcher is concentrating his efforts.
His study in the fields of Classifier under the domain of Pattern recognition overlaps with other disciplines such as Conditional probability distribution.
The Convolutional neural network study combines topics in areas such as Authentication, Cognitive neuroscience of visual object recognition, Leverage and Automatic target recognition.
The study incorporates disciplines such as Iris recognition, Feature, Speech recognition, Reduction and Feature learning in addition to Feature extraction.
While the research belongs to areas of Face, Nasser M. Nasrabadi spends his time largely on the problem of Sketch, intersecting his research to questions surrounding Matching, Embedding and Soft biometrics.
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.
