Deep Learning and Computer Vision Laboratory

   
research-groups-metu-ii-1
 

The Deep Learning and Computer Vision targets to conduct research on the cutting-edge topics of deep learning and computer vision. Research projects vary from human perspective oriented evaluation of systems to development of new algorithm and methods for technical issues. Additionally, experiments on remote and nondestructive image acquisition methods are performed in the laboratory in order to observe the difficulties faced in real data problems. Our research is directed mainly towards the following areas:

  • Object Detection
  • Adversarial Attacks
  • Medical Imaging
  • 3D Model Generation
  • Generative Models
  • GPU programming
 

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Prof. Dr. Alptekin Temizel
atemizel@metu.edu.tr

dlcv-Görkem-Polat-1

Görkem Polat
polatgorkem@gmail.com

dlcv-Oğuz-Hanoğlu-1

Oğuz Hanoğlu
oguz.hanoglu@metu.edu.tr

dlcv-Fatih-Akyön-1

Fatih Akyön
fatih.akyon@obss.com.tr

dlcv-Ümit-Mert-Çağlar-1

Ümit Mert Çağlar
mert.caglar@metu.edu.tr

dlcv-Alperen-İnci-1

Alperen İnci
alperen.inci@metu.edu.tr 

dlcv-Deniz-Şen-1

Deniz Şen
deniz.sen_01@metu.edu.tr

 

dlcv-Berat-Tuna-Karlı-1

Berat Tuna Karlı
tuna.karli@metu.edu.tr

 

 

 

 

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

paper-GPUaccelerated3DESencryption-1

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/j.media.2021.102002, 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

 

contact-1

E-mail: atemizel@metu.edu.tr

Phone: (312) 210 7876

Address: ODTÜ Enformatik Enstitüsü, Oda Z-09, Ankara, Türkiye