What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Computer network
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Image processing, Pattern recognition and Motion estimation.
Artificial intelligence is closely attributed to Algorithm in his study.
A.M. Tekalp interconnects Bayesian network and Robustness in the investigation of issues within Computer vision.
His Image processing research includes themes of Pixel and Key.
His Motion estimation research integrates issues from Image segmentation and Motion compensation.
His Image segmentation research includes elements of Contextual image classification, Object detection and Edge detection.
His most cited work include:
- Lossless generalized-LSB data embedding (874 citations)
- Automatic soccer video analysis and summarization (850 citations)
- High-resolution image reconstruction from lower-resolution image sequences and space-varying image restoration (307 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Pattern recognition, Motion estimation and Algorithm are his primary areas of study.
His is doing research in Image segmentation, Image processing, Image restoration, Feature extraction and Data compression, both of which are found in Artificial intelligence.
As part of his studies on Computer vision, he often connects relevant areas like Kalman filter.
His work in Pattern recognition addresses issues such as Speech recognition, which are connected to fields such as Mel-frequency cepstrum and Feature.
His work on Quarter-pixel motion and Motion field is typically connected to Affine transformation as part of general Motion estimation study, connecting several disciplines of science.
His work in the fields of Algorithm, such as Lossless compression, overlaps with other areas such as Distortion.
He most often published in these fields:
- Artificial intelligence (69.74%)
- Computer vision (57.02%)
- Pattern recognition (25.00%)
What were the highlights of his more recent work (between 2005-2017)?
- Artificial intelligence (69.74%)
- Computer vision (57.02%)
- Speech recognition (10.53%)
In recent papers he was focusing on the following fields of study:
A.M. Tekalp mainly investigates Artificial intelligence, Computer vision, Speech recognition, Hidden Markov model and Real-time computing.
A.M. Tekalp focuses mostly in the field of Artificial intelligence, narrowing it down to topics relating to Computer animation and, in certain cases, Gesture.
His study on Computer vision is mostly dedicated to connecting different topics, such as Video quality.
The Speech recognition study combines topics in areas such as Feature, Mel-frequency cepstrum and Pattern recognition.
In his study, Motion is inextricably linked to Animation, which falls within the broad field of Hidden Markov model.
His Image processing research is multidisciplinary, relying on both Image registration and Homography.
Between 2005 and 2017, his most popular works were:
- Lossless watermarking for image authentication: a new framework and an implementation (236 citations)
- Audiovisual Synchronization and Fusion Using Canonical Correlation Analysis (142 citations)
- Client-Driven Selective Streaming of Multiview Video for Interactive 3DTV (140 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
His scientific interests lie mostly in Artificial intelligence, Speaker recognition, Speech recognition, Computer vision and Data compression.
His Artificial intelligence research focuses on Video compression picture types in particular.
The concepts of his Speech recognition study are interwoven with issues in Biometrics and Feature extraction, Pattern recognition.
A.M. Tekalp has researched Computer vision in several fields, including Key and Video quality.
His work carried out in the field of Data compression brings together such families of science as Multiview Video Coding, Image processing, Message authentication code, Authentication and Scalable Video Coding.
His Image processing study incorporates themes from Lossless compression, Bandwidth allocation and Rate–distortion theory.
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