What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
His primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Pixel and Iterative reconstruction.
His Artificial intelligence study combines topics in areas such as Sequence and Tensor.
Computer vision is a component of his Seam carving, Eye tracking, Video tracking, Image warping and Object detection studies.
His Pattern recognition research incorporates themes from Contextual image classification, Object and Approximation algorithm.
His Pixel study incorporates themes from Binary image and Feature detection.
His Iterative reconstruction research incorporates elements of Motion estimation, Upper and lower bounds and Compositing.
His most cited work include:
- Seam carving for content-aware image resizing (1413 citations)
- Fast Human Detection Using a Cascade of Histograms of Oriented Gradients (1265 citations)
- Ensemble Tracking (1056 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Pattern recognition, Pixel and Image are his primary areas of study.
His Artificial intelligence research includes themes of Point and Computer graphics.
In most of his Computer vision studies, his work intersects topics such as AdaBoost.
Shai Avidan has included themes like Contextual image classification and Similarity in his Pattern recognition study.
His Pixel research is multidisciplinary, incorporating perspectives in Boundary and Image restoration.
His Image research integrates issues from Artificial neural network and Encoder.
He most often published in these fields:
- Artificial intelligence (80.43%)
- Computer vision (57.97%)
- Pattern recognition (28.26%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (80.43%)
- Pattern recognition (28.26%)
- Image (15.22%)
In recent papers he was focusing on the following fields of study:
Shai Avidan mainly focuses on Artificial intelligence, Pattern recognition, Image, Point cloud and Computer vision.
His research on Artificial intelligence often connects related areas such as Code.
His work carried out in the field of Pattern recognition brings together such families of science as Cluster analysis and Graph.
The study incorporates disciplines such as Sampling, Algorithm, Point and Gradient descent in addition to Point cloud.
His study in the field of Ground truth and Pixel also crosses realms of Haze, Underwater and Transmission.
His biological study spans a wide range of topics, including Image plane and Distance transform.
Between 2018 and 2021, his most popular works were:
- Learning to Sample (33 citations)
- Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset. (27 citations)
- Single Image Dehazing Using Haze-Lines (26 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Algorithm
Shai Avidan mainly investigates Artificial intelligence, Pattern recognition, Sampling, Algorithm and Point cloud.
His Artificial intelligence study integrates concerns from other disciplines, such as Proxy and Computer vision.
In general Computer vision, his work in Image plane and RGB color model is often linked to Haze and Radiance linking many areas of study.
His research in Sampling intersects with topics in Point, Task and Code.
Algorithm is often connected to Sample in his work.
The Image study combines topics in areas such as Stereo imaging, Ground truth and Channel.
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