What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
Subhasis Chaudhuri focuses on Artificial intelligence, Computer vision, Image restoration, Image processing and Markov random field.
Subhasis Chaudhuri combines subjects such as Aperture and Pattern recognition with his study of Artificial intelligence.
His research in Computer vision intersects with topics in Zoom and Interpolation.
His work deals with themes such as Regularization, Focus and Spectrogram, which intersect with Image restoration.
His Image processing research incorporates themes from Image registration, Feature and Cross-correlation.
His Markov random field research integrates issues from Estimation theory and Maximum a posteriori estimation.
His most cited work include:
- Detection of blood vessels in retinal images using two-dimensional matched filters (1333 citations)
- Super-resolution image reconstruction (386 citations)
- Super-Resolution Imaging (259 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Computer vision, Pattern recognition, Image and Algorithm.
His study involves Image processing, Pixel, Haptic technology, Feature and Segmentation, a branch of Artificial intelligence.
His study in Haptic technology is interdisciplinary in nature, drawing from both Point cloud and Rendering.
His is doing research in Image resolution, Image restoration, Video tracking, Motion estimation and Tracking, both of which are found in Computer vision.
His studies in Pattern recognition integrate themes in fields like Metric and Cluster analysis.
The various areas that Subhasis Chaudhuri examines in his Hyperspectral imaging study include Visualization and Image fusion.
He most often published in these fields:
- Artificial intelligence (71.27%)
- Computer vision (47.70%)
- Pattern recognition (25.75%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (71.27%)
- Pattern recognition (25.75%)
- Discriminative model (5.15%)
In recent papers he was focusing on the following fields of study:
Subhasis Chaudhuri mainly investigates Artificial intelligence, Pattern recognition, Discriminative model, Haptic technology and Metric.
His studies deal with areas such as Encoder, Machine learning and Computer vision as well as Artificial intelligence.
Subhasis Chaudhuri works in the field of Computer vision, focusing on Rendering in particular.
His Pattern recognition study combines topics in areas such as Embedding, Zero shot learning and Representation.
His Haptic technology study combines topics from a wide range of disciplines, such as Point cloud, Telehaptic, Teleoperation and Adaptive sampling.
His work carried out in the field of Metric brings together such families of science as Algorithm, Similarity, Kernel and Kernel.
Between 2017 and 2021, his most popular works were:
- Multilabel Remote Sensing Image Retrieval Using a Semisupervised Graph-Theoretic Method (66 citations)
- A Novel Technique Based on Deep Learning and a Synthetic Target Database for Classification of Urban Areas in PolSAR Data (45 citations)
- Graph convolutional network for multi-label VHR remote sensing scene recognition (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
His main research concerns Artificial intelligence, Pattern recognition, Discriminative model, Feature extraction and Artificial neural network.
Feature vector, Segmentation, Image and Robustness are the primary areas of interest in his Artificial intelligence study.
His Pattern recognition research incorporates elements of Metric, Embedding, Zero shot learning, Representation and Supervised learning.
His Discriminative model study incorporates themes from Attention network, Convolutional neural network and Biometrics.
The Feature extraction study combines topics in areas such as Classifier, Training set, Data modeling and Feature.
His study looks at the intersection of Artificial neural network and topics like Deep learning with Code, Compressed sensing, Computer vision, Encoder and Data mining.
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