Deep Learning and Computational Neuroscience


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:


  • Object detection
  • Contentious attacks
  • Medical image processing
  • 3D model development
  • Manufacturer models
  • GPU programming


Prof. Dr. Alptekin Temizel


Görkem Polat


Oğuz Hanoğlu


Fatih Akyön


Ümit Mert Çağlar


Alperen İnci 


Deniz Şen



Berat Tuna Karlı





paper-A Dimension Reduction Approach to Player-1

A.E. Aydemir, T. Taskaya Temizel, A. Temizel, K. Preshlenov, D. Strahinov, “A Dimension Reduction Approach to Player Rankings in European Football”, IEEE Access, doi: 10.1109/ACCESS.2021.3107585, Aug. 2021


K.F. Altınok, A. Peker, C. Tezcan, A. Temizel, “GPU accelerated 3DES encryption”, Concurrency and Computation: Practice and Experience, doi:10.1002/cpe.6507, July 2021

paper-LPMNet Latent Part Modification and Generation-1

C. Ongun, A.Temizel, “LPMNet: Latent Part Modification and Generation for 3D Point Clouds”, Computers & Graphics, doi:10.1016/j.cag.2021.02.006, vol. 96, pp. 1-13, May 2021

paper-Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy-1

S. Ali, M. Dmitrieva, N. Ghatwary, S. Bano, G. Polat, A. Temizel, et al., “Deep Learning for Detection and Segmentation of Artefact and Disease Instances in Gastrointestinal Endoscopy”, Medical Image Analysis, doi:10.1016/, vol. 70, May 2021

paper-Multi-modal Egocentric Activity Recognition using-1

M.A. Arabacı, F. Özkan, E. Surer, Peter Jancovic, A. Temizel, “Multi-modal Egocentric Activity Recognition using Multi-Kernel Learning”, Multimedia Tools and Applications, vol. 80 no. 11, pp. 16299–16328, doi:10.1007/s11042-020-08789-7, April 2021

paper-Imperceptible Adversarial Examples by Spatial Chroma-Shift-1

A. Aydin, D. Sen, B.T. Karli, O. Hanoglu, A. Temizel, “Imperceptible Adversarial Examples by Spatial Chroma-Shift”, ACM Multimedia 2021, Workshop on Adversarial Learning for Multimedia, Oct. 2021

paper-Generative Data Augmentation for Vehicle-1

H. Kumdakçı, C. Öngün, A. Temizel, “Generative Data Augmentation for Vehicle Detection in Aerial Images”, International Conference on Pattern Recognition (ICPR), Workshop on Analysis of Aerial Motion, Jan. 2021