What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
Amir Said mainly investigates Data compression, Algorithm, Artificial intelligence, Theoretical computer science and Image compression.
The various areas that Amir Said examines in his Data compression study include Set partitioning in hierarchical trees and Data set.
His work carried out in the field of Algorithm brings together such families of science as Transform coding and Bandwidth compression.
His Transform coding research is multidisciplinary, incorporating elements of Entropy encoding and Wavelet transform.
The Artificial intelligence study combines topics in areas such as Computer vision and Pattern recognition.
His work deals with themes such as Lossy compression, Lossless compression, Scale-space segmentation, Segmentation-based object categorization and Compression, which intersect with Image compression.
His most cited work include:
- A new, fast, and efficient image codec based on set partitioning in hierarchical trees (5207 citations)
- An image multiresolution representation for lossless and lossy compression (606 citations)
- Efficient, low-complexity image coding with a set-partitioning embedded block coder (332 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Algorithm, Data compression and Theoretical computer science are his primary areas of study.
Matching, Decision rule, Feature and Microphone is closely connected to Pattern recognition in his research, which is encompassed under the umbrella topic of Artificial intelligence.
His Computer vision study which covers Computer graphics that intersects with Light field.
His Algorithm research incorporates themes from Reverberation, Encoder, Coding and Set partitioning in hierarchical trees.
The Data compression study combines topics in areas such as Transform coding and Image compression.
The concepts of his Transform coding study are interwoven with issues in Wavelet and Wavelet transform.
He most often published in these fields:
- Artificial intelligence (43.71%)
- Computer vision (33.11%)
- Algorithm (21.19%)
What were the highlights of his more recent work (between 2010-2016)?
- Artificial intelligence (43.71%)
- Computer vision (33.11%)
- Algorithm (21.19%)
In recent papers he was focusing on the following fields of study:
Amir Said spends much of his time researching Artificial intelligence, Computer vision, Algorithm, Context-adaptive binary arithmetic coding and Data compression.
Amir Said studied Artificial intelligence and Pattern recognition that intersect with Image resolution and Encoding.
His study in the field of Scalar quantization also crosses realms of Exploit.
Amir Said is interested in Lossless compression, which is a field of Data compression.
His research in Context-adaptive variable-length coding tackles topics such as Transform coding which are related to areas like Harmonic Vector Excitation Coding.
His Entropy encoding study incorporates themes from Huffman coding and Arithmetic coding.
Between 2010 and 2016, his most popular works were:
- No-Reference Blur Assessment of Digital Pictures Based on Multifeature Classifiers (117 citations)
- A Steered-Response Power Algorithm Employing Hierarchical Search for Acoustic Source Localization Using Microphone Arrays (40 citations)
- Digital Signal Compression: Principles and Practice (30 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
Amir Said focuses on Reverberation, Computer vision, Artificial intelligence, Light field and Computer graphics.
His studies in Reverberation integrate themes in fields like Speech recognition, Speech processing, Algorithm, Feature extraction and Robustness.
His work carried out in the field of Algorithm brings together such families of science as Blind signal separation and Audio signal.
In general Artificial intelligence, his work in Pattern recognition is often linked to Depth perception linking many areas of study.
The study incorporates disciplines such as Virtual image, Parallax and Visualization in addition to Light field.
His work on Stereo display as part of general Computer graphics research is often related to Object, thus linking different fields of science.
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