What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Image segmentation.
His Artificial intelligence study often links to related topics such as Machine learning.
His work in Computer vision addresses issues such as Representation, which are connected to fields such as Activity recognition and Motion detection.
He interconnects Image resolution, Subspace topology, Similarity, Matching and Feature detection in the investigation of issues within Pattern recognition.
His Feature extraction research includes elements of Feature, Support vector machine, Feature vector, Facial expression and Genetic programming.
His work deals with themes such as Shadow and Thresholding, which intersect with Image segmentation.
His most cited work include:
- Individual recognition using gait energy image (1235 citations)
- Automatic Target Recognition: State of the Art Survey (357 citations)
- 3D free-form object recognition in range images using local surface patches (346 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Machine learning.
His work is connected to Cognitive neuroscience of visual object recognition, Image processing, Image segmentation, Pattern recognition and Synthetic aperture radar, as a part of Artificial intelligence.
His research in Face, Facial recognition system, Object, Object detection and Video tracking are components of Computer vision.
The Face study combines topics in areas such as Gait and Biometrics.
His research integrates issues of Image resolution and Facial expression in his study of Facial recognition system.
Bir Bhanu studied Pattern recognition and Feature that intersect with Image retrieval.
He most often published in these fields:
- Artificial intelligence (82.67%)
- Computer vision (51.99%)
- Pattern recognition (34.32%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (82.67%)
- Computer vision (51.99%)
- Pattern recognition (34.32%)
In recent papers he was focusing on the following fields of study:
Bir Bhanu mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Deep learning and Convolutional neural network.
His Machine learning research extends to Artificial intelligence, which is thematically connected.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Subspace topology, Similarity, Feature, Identification and Matching.
His Feature study integrates concerns from other disciplines, such as Histogram and Representation.
Bir Bhanu focuses mostly in the field of Deep learning, narrowing it down to topics relating to Biometrics and, in certain cases, Embedding.
As a part of the same scientific family, Bir Bhanu mostly works in the field of Convolutional neural network, focusing on Stem cell and, on occasion, Induced pluripotent stem cell and Cell type.
Between 2014 and 2021, his most popular works were:
- Person Reidentification With Reference Descriptor (72 citations)
- Person Re-Identification by Robust Canonical Correlation Analysis (58 citations)
- Efficient smile detection by Extreme Learning Machine (51 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Bir Bhanu focuses on Artificial intelligence, Pattern recognition, Computer vision, Classifier and Deep learning.
His Artificial intelligence study combines topics in areas such as Matching and Machine learning.
His work carried out in the field of Pattern recognition brings together such families of science as Artificial neural network, Similarity and Identification.
His Similarity research integrates issues from Salient, Data mining, Set, Statistical model and Pattern recognition.
The various areas that he examines in his Computer vision study include Focus and Crowds.
His work focuses on many connections between Classifier and other disciplines, such as Extreme learning machine, that overlap with his field of interest in Support vector machine, Facial expression, Linear discriminant analysis and Feature vector.
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