Deep Learning and Computer Vision Lab aims to conduct research on cutting-edge topics such as deep learning and computer vision. Research projects range from the evaluation of systems from a human perspective to the development of new algorithms and methods for technical issues. Furthermore, experiments on remote and non-destructive image acquisition methods are carried out in the laboratory to observe the challenges which are faced in real data problems. Our research is mainly oriented towards the following areas:

A Dimension Reduction Approach to Player Rankings in European Football
Ayse Elvan Aydemir; Tugba Taskaya Temizel; Alptekin Temizel; Kliment Preshlenov; Daniel M. Strahinov
24 August 2021

GPU accelerated 3DES encryption
Kaan Furkan Altınok, Afşin Peker, Cihangir Tezcan, Alptekin Temizel
21 July 2021

LPMNet: Latent part modification and generation for 3D point clouds
Cihan Öngün, Alptekin Temizel
May 2021

S. Ali, M. Dmitrieva, N. Ghatwary, S. Bano, G. Polat, A. Temizel
May 2021

Multi-modal egocentric activity recognition using multi-kernel learning
Mehmet Ali Arabacı, Fatih Özkan, Elif Surer, Peter Jančovič, Alptekin Temizel
28 April 2020

Imperceptible Adversarial Examples by Spatial Chroma-Shift
Ayberk Aydin, Deniz Sen, Berat Tuna Karli, Oguz Hanoglu, Alptekin Temizel
22 October 2021

Generative Data Augmentation for Vehicle Detection in Aerial Images
Hilmi Kumdakcı, Cihan Öngün, Alptekin Temizel
Jan. 2021