VPA - Computer Vision and Pattern Analysis Laboratory
Welcome to the Computer Vision and Pattern Analysis Laboratory of Sabanci University.
The Computer Vision and Pattern Analysis Lab of Sabanci University was established in 2001 by Prof. Aytül Erçil, and was selected as a "potential center of excellence" by the European Union. Its founding objective has been designing and implementing industrially applicable solutions using computer vision and pattern analysis techniques that are also theoretically fulfilling.
The members of VPALab work closely with local and foreign universities, governmental and private organizations to foster high-technology growth in the region. This growth is facilitated through substantive research, from theory to practical application and the training of skilled graduate and undergraduate engineers and scientists.
The main sources of research funding for VPALab include EU programs, EUREKA, NSF, government agencies and the industry. It is clearly recognized that many of the exciting new developments occurring in the convergent digital era require more integrated, collaborative and cross-disciplinary approaches. In recognition of this, the collaborative efforts at VPALab range not only cross discipline among various faculty members within the group, but also include other groups (e.g. Bioinformatics, Material Science, Mechatronics, Microelectronics programmes of Sabanci University) and other Universities (national and international).
For more information please check our Research and Publications pages.
Latest News from VPA
May 2023: A new event has been added SIU2023 - NST
Apr. 2023: A webinar about the use of technology and remote sensing in particular for disaster management by B. Yanikoglu, I. Tekin and E. Aptoula (in Turkish) LINK
Jan. 2023: E. Aptoula has been appointed as an Associated Editor for the IEEE Transactions on Geoscience and Remote Sensing journal.
Nov. 2022: A new Tübitak 1001 project has been awarded to Prof. Berrin Yanikoglu on the "Automatic transcription of Ottoman Turkish documents through deep learning".
Nov. 2022: Our joint paper with the University of Lincoln on domain generalization for object detection has just been accepted to AAAI'23.