Graph Transduction and Model-driven Visual Dictionary Construction with Applications in Ancient Coin Classification and Computer Vision

Day - Time: 12 October 2018, h.11:00
Place: Area della Ricerca CNR di Pisa - Room: C-29
  • Sinem Aslan (European Centre of Living Technology (ECLT), Università Cà Foscari Venezia)

Ercan Engin Kuruoglu


This talk will present two of our works in the field of computer vision. I will first introduce our recent work on ancient coin classification using Graph Transduction Games (GTG). Recognizing the type of an ancient coin requires theoretical expertise and years of experience in the field of numismatics. A common way to detect the period of a discovered coin is searching through the manual books where ancient coins are indexed which requires a highly time-consuming and demanding labor. Automatizing such a manual procedure not only provides faster processing time but can also support historians and archaeologists for a more accurate decision. In order to automatize such a task, we proposed to model ancient coin image classification using a nonparametric semi-supervised learning approach, namely Graph Transduction Games (GTG). In this part, I will first introduce the challenges on this particular problem from the computer vision point of view and present our solution.
Then, I will briefly lead into a previous work, i.e. a model-driven visual dictionary construction technique named SymPaD that is developed for image understanding applications. At SymPaD, dictionary building starts from a core of shape primitives, which have commonalities with the shape models envisaged by the earliest to the latest proponents of the idea, i.e., from Marr to Griffin. We then proceed to enrich the dictionary by using detailed parametrization of the shape space and by applying nonlinear dyadic compositions. Compared with the existing model-driven schemes, our method is able to represent and characterize images in various image understanding applications with competitive, and often better performance.

Sinem Aslan is a visiting postdoctoral scholar at European Centre of Living Technology (ECLT) of Caâ?? Foscari University of Venice since May 2018. Prior to that, she was a postdoctoral researcher in Imaging and Vision Laboratory (IVL) at Department of Informatics, Systems and Communication at University of Milano-Bicocca for one year. In IVL, she has been working on food images segmentation for automatic dietary assessment applications.
She obtained her Ph.D. degree (October 2016) in International Computer Institute, Ege University, Turkey, under supervisions of Prof. Bülent Sankur and Prof. E. Turhan Tunali. She mainly investigated model-based visual dictionary techniques for image understanding applications in her thesis. During her Ph.D. studies, she held a visiting position in BUSIM laboratory at Bogaziçi University, Turkey, for two semesters. Prior to that, she received her MSc. degree (2007) in International Computer Institute from Ege University and BSc. degree in Department of Electronics Engineering from Ankara University. Beyond her research background, she has a research/teaching assistant experience (in Turkey) for 13 years.
She is a reviewer for various international journals such as IEEE Transactions on Image Processing, Multimedia Tools and Applications, Pattern Analysis and Applications, Signal, Image and Video Processing, IET Image Processing, Turkish Journal of Electrical Engineering and Computer Sciences and for the conference IEEE SIU.