What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Operating system
Amir Averbuch spends much of his time researching Artificial intelligence, Computer vision, Algorithm, Image processing and Pattern recognition.
Amir Averbuch works mostly in the field of Artificial intelligence, limiting it down to topics relating to Speech recognition and, in certain cases, IBM 8514 and IBM, as a part of the same area of interest.
Computer vision is closely attributed to Compressed sensing in his study.
Amir Averbuch has researched Algorithm in several fields, including Image quality, Theoretical computer science, Matrix, Embedding and Interpolation.
His study looks at the relationship between Image processing and topics such as Thresholding, which overlap with Change detection, Combinatorics, Time complexity, Ackermann function and Disjoint-set.
Amir Averbuch combines subjects such as Data mining and Cluster analysis with his study of Pattern recognition.
His most cited work include:
- Interacting multiple model methods in target tracking: a survey (873 citations)
- Image compression using wavelet transform and multiresolution decomposition (190 citations)
- Automatic segmentation of moving objects in video sequences: a region labeling approach (167 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Algorithm, Artificial intelligence, Wavelet, Computer vision and Pattern recognition.
His Algorithm research is multidisciplinary, incorporating perspectives in Biorthogonal system, Wavelet transform and Mathematical optimization.
His Artificial intelligence study focuses mostly on Motion estimation, Image processing, Pixel, Dimensionality reduction and Image compression.
While the research belongs to areas of Dimensionality reduction, he spends his time largely on the problem of Cluster analysis, intersecting his research to questions surrounding Diffusion map.
As part of one scientific family, Amir Averbuch deals mainly with the area of Wavelet, narrowing it down to issues related to the Spline, and often Spline wavelet.
His is doing research in Image registration and Segmentation, both of which are found in Computer vision.
He most often published in these fields:
- Algorithm (34.58%)
- Artificial intelligence (27.08%)
- Wavelet (16.89%)
What were the highlights of his more recent work (between 2014-2021)?
- Algorithm (34.58%)
- Artificial intelligence (27.08%)
- Wavelet (16.89%)
In recent papers he was focusing on the following fields of study:
Amir Averbuch spends much of his time researching Algorithm, Artificial intelligence, Wavelet, Spline and Pattern recognition.
His Algorithm study combines topics from a wide range of disciplines, such as Wavelet transform, Wavelet packet decomposition, Embedding, Mathematical optimization and Signal processing.
In his research on the topic of Wavelet packet decomposition, RGB color model is strongly related with Image processing.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Computer vision.
His Wavelet study combines topics in areas such as Time domain, Fast Fourier transform, Filter and Network packet.
The various areas that Amir Averbuch examines in his Spline study include Orthonormal basis, Spline wavelet, Smoothing spline and Matrix polynomial.
Between 2014 and 2021, his most popular works were:
- Analysis of Human Brain Structure Reveals that the Brain "Types" Typical of Males Are Also Typical of Females, and Vice Versa. (54 citations)
- Anomaly detection in dynamically evolving data and systems (43 citations)
- MultiView Diffusion Maps. (34 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Operating system
The scientist’s investigation covers issues in Algorithm, Diffusion map, Data mining, Dimensionality reduction and Artificial intelligence.
His Algorithm research incorporates elements of Random matrix, Rank, Mathematical optimization, LU decomposition and Extension.
Amir Averbuch works mostly in the field of Diffusion map, limiting it down to topics relating to Kernel method and, in certain cases, Euclidean distance matrix, Euclidean distance and Mathematical analysis.
His Dimensionality reduction research includes themes of Pixel, Cluster analysis and k-nearest neighbors algorithm.
The Artificial intelligence study combines topics in areas such as Machine learning, Computer vision and Pattern recognition.
His Wavelet study, which is part of a larger body of work in Computer vision, is frequently linked to Imaging lens, bridging the gap between disciplines.
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