What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Computer network
His main research concerns Algorithm, Filter bank, Wavelet, Wavelet transform and Signal processing.
His studies deal with areas such as Transform coding, Mathematical optimization, Computer vision and Signal reconstruction as well as Algorithm.
His Filter bank research incorporates themes from Filter, Filter, Filter design, Control theory and Digital filter.
His Wavelet research includes themes of Basis and Pure mathematics.
His Wavelet transform research entails a greater understanding of Artificial intelligence.
His research investigates the connection with Artificial intelligence and areas like Pattern recognition which intersect with concerns in Image retrieval.
His most cited work include:
- The contourlet transform: an efficient directional multiresolution image representation (3282 citations)
- Wavelets and signal processing (2406 citations)
- Adaptive wavelet thresholding for image denoising and compression (2344 citations)
What are the main themes of his work throughout his whole career to date?
Martin Vetterli spends much of his time researching Algorithm, Artificial intelligence, Computer vision, Wavelet and Mathematical optimization.
Martin Vetterli has researched Algorithm in several fields, including Theoretical computer science, Signal reconstruction, Signal processing, Sampling and Filter bank.
His Filter bank research integrates issues from Digital filter, Filter and Filter.
His Artificial intelligence research is multidisciplinary, relying on both Computer graphics and Pattern recognition.
His study in Wavelet transform, Discrete wavelet transform, Wavelet packet decomposition and Stationary wavelet transform falls under the purview of Wavelet.
His research integrates issues of Transform coding and Image compression in his study of Data compression.
He most often published in these fields:
- Algorithm (42.57%)
- Artificial intelligence (21.34%)
- Computer vision (17.10%)
What were the highlights of his more recent work (between 2012-2021)?
- Algorithm (42.57%)
- Artificial intelligence (21.34%)
- Computer vision (17.10%)
In recent papers he was focusing on the following fields of study:
Martin Vetterli mostly deals with Algorithm, Artificial intelligence, Computer vision, Mathematical optimization and Compressed sensing.
The various areas that Martin Vetterli examines in his Algorithm study include Set, Sampling, Signal, Trajectory and Euclidean distance.
His research on Sampling also deals with topics like
- Bandlimiting which intersects with area such as Signal reconstruction and Encoding,
- Path most often made with reference to Parallel.
The Artificial intelligence study combines topics in areas such as Quadratic growth and Pattern recognition.
His Mathematical optimization research is multidisciplinary, incorporating elements of Estimator, Inverse problem and Robustness.
Martin Vetterli has included themes like Position and Signal processing in his Wireless sensor network study.
Between 2012 and 2021, his most popular works were:
- Euclidean Distance Matrices: Essential theory, algorithms, and applications (176 citations)
- Acoustic echoes reveal room shape (163 citations)
- Near-Optimal Sensor Placement for Linear Inverse Problems (138 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Algorithm
His primary areas of investigation include Algorithm, Artificial intelligence, Computer vision, Mathematical optimization and Compressed sensing.
His Algorithm study incorporates themes from Signal, Microphone, Sampling, Iterative reconstruction and Euclidean distance.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning, Ultrasonic sensor, Process and Scaling.
His studies in Computer vision integrate themes in fields like Calibration Error, Ultrasound Tomography, Set and Matrix completion.
His Mathematical optimization study integrates concerns from other disciplines, such as Noise measurement, Sparse matrix, Robustness and Moore–Penrose pseudoinverse.
His Compressed sensing study combines topics in areas such as Underdetermined system, Probability distribution, Random matrix, Combinatorics and Block matrix pseudoinverse.
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