What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Statistics
Antonio Ortega mostly deals with Algorithm, Artificial intelligence, Data compression, Computer vision and Mathematical optimization.
His Algorithm study integrates concerns from other disciplines, such as Encoder, Graph bandwidth, Interpolation, Adjacency matrix and Distortion.
He combines subjects such as Graph and Pattern recognition with his study of Artificial intelligence.
The various areas that Antonio Ortega examines in his Data compression study include Transform coding, Wavelet, Wavelet transform and Quantization.
His study in Computer vision is interdisciplinary in nature, drawing from both Coding and Sub-band coding.
His Graph theory research focuses on Signal processing and how it connects with Digital television.
His most cited work include:
- The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains (2237 citations)
- Rate-distortion methods for image and video compression (768 citations)
- Graph Signal Processing: Overview, Challenges, and Applications (559 citations)
What are the main themes of his work throughout his whole career to date?
Antonio Ortega spends much of his time researching Algorithm, Artificial intelligence, Computer vision, Data compression and Graph.
His Algorithm study incorporates themes from Encoder, Theoretical computer science, Wavelet and Graph.
His biological study spans a wide range of topics, including Real-time computing and Distortion.
His work carried out in the field of Artificial intelligence brings together such families of science as Coding and Pattern recognition.
His study in the fields of Depth map, Motion compensation, Image processing and View synthesis under the domain of Computer vision overlaps with other disciplines such as Multiview Video Coding.
His Data compression research incorporates themes from Transform coding and Wireless sensor network.
He most often published in these fields:
- Algorithm (40.46%)
- Artificial intelligence (31.49%)
- Computer vision (18.89%)
What were the highlights of his more recent work (between 2014-2021)?
- Algorithm (40.46%)
- Graph (12.60%)
- Graph (9.16%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Algorithm, Graph, Graph, Artificial intelligence and Laplacian matrix.
His research integrates issues of Discrete cosine transform, Sampling, Separable space, Mathematical optimization and Coding in his study of Algorithm.
His work carried out in the field of Coding brings together such families of science as Data compression, Decoding methods and Graph based.
His studies in Graph integrate themes in fields like Graph signal processing, Signal processing, Matrix decomposition, Fourier transform and Wavelet.
The concepts of his Graph study are interwoven with issues in Semi-supervised learning and Theoretical computer science.
His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning, Computer vision and Pattern recognition.
Between 2014 and 2021, his most popular works were:
- Graph Signal Processing: Overview, Challenges, and Applications (559 citations)
- Efficient Sampling Set Selection for Bandlimited Graph Signals Using Graph Spectral Proxies (224 citations)
- Multiresolution Graph Fourier Transform for Compression of Piecewise Smooth Images (158 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Statistics
Antonio Ortega focuses on Algorithm, Graph, Artificial intelligence, Theoretical computer science and Discrete cosine transform.
His Algorithm research includes themes of Transform coding, Laplacian matrix, Graph, Graph bandwidth and Mathematical optimization.
His research integrates issues of Semi-supervised learning, Graph signal processing, Signal processing, Separable space and Data point in his study of Graph.
The Artificial intelligence study combines topics in areas such as Covariance function, Computer vision and Pattern recognition.
His Theoretical computer science study combines topics from a wide range of disciplines, such as Channel code, Decoding methods, Distributed source coding, Constant function and Deep learning.
Antonio Ortega combines subjects such as Decimation, Coding and Cut with his study of Discrete cosine transform.
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