What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer network
- Computer vision
Yucel Altunbasak spends much of his time researching Artificial intelligence, Computer vision, Motion estimation, Image processing and Image quality.
Artificial intelligence is closely attributed to Pattern recognition in his research.
As part of his studies on Computer vision, Yucel Altunbasak often connects relevant areas like Algorithm.
His Motion field study in the realm of Motion estimation interacts with subjects such as Affine transformation.
The study incorporates disciplines such as Histogram, Histogram matching, Visual artifact and White noise in addition to Image processing.
The study incorporates disciplines such as RGB color model and Color filter array in addition to Color image.
His most cited work include:
- Color plane interpolation using alternating projections (598 citations)
- A Histogram Modification Framework and Its Application for Image Contrast Enhancement (581 citations)
- Demosaicking: color filter array interpolation (498 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Algorithm, Real-time computing and Computer network.
His study looks at the intersection of Artificial intelligence and topics like Pattern recognition with Markov model.
Much of his study explores Computer vision relationship to Interpolation.
His Algorithm research integrates issues from Codec, Encoder and Theoretical computer science.
His studies in Computer network integrate themes in fields like Wireless and Distributed computing.
His Demosaicing study combines topics in areas such as RGB color model and Color filter array.
He most often published in these fields:
- Artificial intelligence (47.57%)
- Computer vision (39.32%)
- Algorithm (20.87%)
What were the highlights of his more recent work (between 2008-2017)?
- Artificial intelligence (47.57%)
- Computer vision (39.32%)
- IPTV (6.31%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Computer vision, IPTV, Computer network and Algorithm.
His Artificial intelligence study frequently draws connections between related disciplines such as Distortion.
His work carried out in the field of Computer vision brings together such families of science as Nearest-neighbor interpolation, Color filter array and Interpolation.
In his study, RGB color model is strongly linked to Color image, which falls under the umbrella field of Color filter array.
His studies deal with areas such as Unicast, Transmission, Server-side, Communication channel and Video on demand as well as IPTV.
His Algorithm research is multidisciplinary, relying on both Smoothness, Heuristics and Bayesian probability, Bayes' theorem.
Between 2008 and 2017, his most popular works were:
- A Histogram Modification Framework and Its Application for Image Contrast Enhancement (581 citations)
- Multiscale Gradients-Based Color Filter Array Interpolation (96 citations)
- Edge Strength Filter Based Color Filter Array Interpolation (77 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer network
- Computer vision
Yucel Altunbasak mainly focuses on Computer vision, Artificial intelligence, Algorithm, Color filter array and Demosaicing.
His Interpolation research extends to Computer vision, which is thematically connected.
His work on Dynamic programming is typically connected to Pairing, A priori and a posteriori and Antiparallel as part of general Algorithm study, connecting several disciplines of science.
His Color filter array research incorporates themes from Bayer filter and Filter.
His Bayer filter research is multidisciplinary, incorporating perspectives in RGB color model, Pixel, Color co-site sampling and Color difference.
His Image processing research includes elements of Nearest-neighbor interpolation, Multivariate interpolation, Image segmentation and Image quality.
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