What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
His primary areas of investigation include Artificial intelligence, Computer vision, Image processing, Algorithm and Signature.
Yehoshua Y. Zeevi combines subjects such as Signal and Pattern recognition with his study of Artificial intelligence.
Yehoshua Y. Zeevi frequently studies issues relating to Adaptive filter and Computer vision.
His studies in Image processing integrate themes in fields like Nonuniform sampling, Representation, Bandwidth, Multidimensional signal processing and Iterative reconstruction.
His work carried out in the field of Algorithm brings together such families of science as Zak transform, Mathematical optimization, Oversampling, Signal processing and Piecewise.
His Signature research is multidisciplinary, incorporating elements of Multimedia, Metadata and Cluster analysis.
His most cited work include:
- The generalized Gabor scheme of image representation in biological and machine vision (421 citations)
- Image enhancement and denoising by complex diffusion processes (395 citations)
- Forward-and-backward diffusion processes for adaptive image enhancement and denoising (301 citations)
What are the main themes of his work throughout his whole career to date?
Yehoshua Y. Zeevi spends much of his time researching Artificial intelligence, Computer vision, Algorithm, Signature and Multimedia.
His Artificial intelligence study incorporates themes from Blind signal separation and Pattern recognition.
His Algorithm research is multidisciplinary, incorporating elements of Frame, Representation, Zak transform, Mathematical optimization and Oversampling.
The various areas that Yehoshua Y. Zeevi examines in his Signature study include Metadata, Data mining, Matching, Cluster analysis and Information retrieval.
His study looks at the relationship between Multimedia and topics such as Content, which overlap with Element.
His Image processing study integrates concerns from other disciplines, such as Anisotropic diffusion, Noise reduction and Mathematical analysis.
He most often published in these fields:
- Artificial intelligence (40.98%)
- Computer vision (25.39%)
- Algorithm (20.94%)
What were the highlights of his more recent work (between 2015-2020)?
- Signature (19.15%)
- Content (16.26%)
- Multimedia (18.93%)
In recent papers he was focusing on the following fields of study:
His main research concerns Signature, Content, Multimedia, Element and Metadata.
His Signature research includes themes of Matching, Information retrieval, Data mining and Preference.
His study looks at the relationship between Matching and fields such as Representation, as well as how they intersect with chemical problems.
He combines subjects such as Analytics and Cluster analysis with his study of Multimedia.
His Cluster analysis study necessitates a more in-depth grasp of Artificial intelligence.
His study on Metadata is covered under World Wide Web.
Between 2015 and 2020, his most popular works were:
- System and method for capturing a multimedia content item by a mobile device and matching sequentially relevant content to the multimedia content item (47 citations)
- Statistics of Natural Stochastic Textures and Their Application in Image Denoising (16 citations)
- SYSTEM AND METHOD FOR DETERMINING PARAMETERS BASED ON MULTIMEDIA CONTENT (8 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
Yehoshua Y. Zeevi mainly investigates Signature, Content, Element, Information retrieval and Metadata.
His Signature research includes elements of Augmented reality, Computer vision, Artificial intelligence and Character.
His work in Content addresses subjects such as Multimedia, which are connected to disciplines such as Matching.
His Matching study combines topics in areas such as World Wide Web, Mobile device and Index.
His work carried out in the field of Information retrieval brings together such families of science as Preference and User profile.
Many of his studies involve connections with topics such as Computer hardware and Metadata.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
