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Fatih Cagatay Akyon
Researcher at Bilkent University
Publications - 19
Citations - 120
Fatih Cagatay Akyon is an academic researcher from Bilkent University. The author has contributed to research in topics: Computer science & Iterative reconstruction. The author has an hindex of 4, co-authored 14 publications receiving 38 citations. Previous affiliations of Fatih Cagatay Akyon include ASELSAN & Middle East Technical University.
Papers
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Proceedings ArticleDOI
Image deconvolution via efficient sparsifying transform learning
TL;DR: A data-adaptive sparse image reconstruction approach for image deconvolution based on transform learning which adaptively learns a patch-based sparsifying transform and simultaneously reconstructs the image from its noisy blurred measurement.
Book ChapterDOI
Drone-vs-Bird Detection Challenge at ICIAP 2021
Angelo Coluccia,Alessio Fascista,Arne Schumann,Lars Sommer,Anastasios Dimou,Dimitrios Zarpalas,Nabin Sharma,Mrunalini Nalamati,Ogulcan Eryuksel,Kamil Anil Ozfuttu,Fatih Cagatay Akyon,Kadir Sahin,Efe Buyukborekci,Devrim Cavusoglu,Sinan O. Altinuc,Da Xing,Halil Utku Unlu,Nikolaos Evangeliou,Anthony Tzes,Abhijeet Nayak,Mondher Bouazizi,Tasweer Ahmad,Artur Goncalves,Bastien Rigault,Raghvendra Jain,Yutaka Matsuo,Helmut Prendinger,Edmond Jajaga,Veton Rushiti,Blerant Ramadani,Daniel Pavleski +30 more
Posted Content
Track Boosting and Synthetic Data Aided Drone Detection.
TL;DR: In this article, a Kalman-based object tracker was used to improve the performance of a YOLOv5 model with real and synthetically generated data using an optimal subset of synthetic data.
Proceedings ArticleDOI
Effect of different sparsity priors on compressive photon-sieve spectral imaging
TL;DR: The image formation model and a sparsity-based reconstruction approach are presented for compressive photon-sieve spectral imager and results show promising image reconstruction performance from these compressive measurements.
Journal ArticleDOI
Artificial intelligence-supported web application design and development for reducing polypharmacy side effects and supporting rational drug use in geriatric patients
TL;DR: In this paper , an Artificial Intelligence (AI) supported web application was designed and developed to facilitate the practical use of the tool, which analyzes age, drugs, and diseases specifically for the patient 60 times faster than the manual method.