What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
His primary areas of study are Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Image processing.
His work on Artificial intelligence deals in particular with Motion estimation, Iterative reconstruction, Eye tracking, Video tracking and Motion.
His Computer vision study frequently draws parallels with other fields, such as Computer graphics.
His studies deal with areas such as Facial recognition system, Solid modeling and Benchmark as well as Pattern recognition.
The concepts of his Algorithm study are interwoven with issues in Voronoi diagram, Boundary, Optimal estimation and Rank.
Narendra Ahuja has researched Image processing in several fields, including Bilinear interpolation, Discrete cosine transform, Hadamard transform, Invariant and Matrix multiplication.
His most cited work include:
- Detecting faces in images: a survey (3268 citations)
- Single image super-resolution from transformed self-exemplars (1141 citations)
- Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution (904 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Image segmentation.
His Segmentation, Image processing, Pixel, Motion estimation and Image investigations are all subjects of Artificial intelligence research.
His Motion estimation study combines topics from a wide range of disciplines, such as Motion and Motion analysis.
Computer vision is closely attributed to Computer graphics in his research.
His Pattern recognition research incorporates elements of Machine learning and Feature.
His research on Algorithm frequently connects to adjacent areas such as Mathematical optimization.
He most often published in these fields:
- Artificial intelligence (76.75%)
- Computer vision (55.31%)
- Pattern recognition (28.86%)
What were the highlights of his more recent work (between 2009-2021)?
- Artificial intelligence (76.75%)
- Computer vision (55.31%)
- Pattern recognition (28.86%)
In recent papers he was focusing on the following fields of study:
Narendra Ahuja mostly deals with Artificial intelligence, Computer vision, Pattern recognition, Convolutional neural network and Pixel.
His studies in Artificial intelligence integrate themes in fields like Frame and Machine learning.
His Computer vision research includes themes of Algorithm and Robustness.
The various areas that Narendra Ahuja examines in his Pattern recognition study include Contextual image classification and Artificial neural network.
The Convolutional neural network study combines topics in areas such as Image resolution, Bicubic interpolation, Feature, Pyramid and Iterative reconstruction.
His research in Pixel tackles topics such as Chromaticity which are related to areas like Invariant and Real image.
Between 2009 and 2021, his most popular works were:
- Single image super-resolution from transformed self-exemplars (1141 citations)
- Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution (904 citations)
- Robust visual tracking via multi-task sparse learning (576 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
Narendra Ahuja focuses on Artificial intelligence, Computer vision, Pattern recognition, Image and Image resolution.
His study in Convolutional neural network, Video tracking, Eye tracking, Sparse approximation and Feature extraction is carried out as part of his studies in Artificial intelligence.
Computer vision is closely attributed to Algorithm in his study.
His biological study spans a wide range of topics, including Contextual image classification, Invariant and Benchmark.
His work deals with themes such as Planar projection, Structure and Planar, which intersect with Image.
His Image resolution research incorporates themes from RGB color model and Upsampling.
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