Seminar: Quantum Cuts : A Quantum Mechanical Spectral Graph Partitioning Method for Salient Object Detection, Çağlar Aytekin, Tampere University of Technology
Çağlar Aytekin, Department of Signal Processing, Tampere University of Technology
Title: Quantum Cuts : A Quantum Mechanical Spectral Graph Partitioning Method for Salient Object Detection
Date: October 6, 2016 Thursday
Place: Seminar Hall, Computer Engineering Department
Salient Object Detection can be defined as highlighting the objects that visually stand out from the rest of the environment. The growing interest in this topic can be explained by its many direct applications such as image and video coding, summarization and object tracking. Moreover, it can be used to reduce the complexity of higher level Computer Vision tasks by reducing their search space. This talk focuses on this important and emerging Computer Vision task and describes novel approaches and methods for salient object detection in images and videos.
First part of the talk is constituted of a direct application of ideas originally proposed for describing the wave nature of particles in Quantum Mechanics and expressed through Schrödinger's Equation, to salient object detection in images. Second part introduces a spectral graph based salient object detection method, namely Quantum Cuts. The proposed method adopts a novel approach to spectral graph partitioning by integrating quantum mechanical concepts to Spectral Graph Theory. The third part of the talk covers improvements and extensions on Quantum Cuts by analyzing the main factors that affect its performance in salient object detection. Finally future directions in salient object detection research will be discussed.
Çağlar Aytekin was born in Turkey in 1987. He received the B.Sc. degree from the Electrical and Electronics Engineering Department from Middle East Technical University, Ankara Turkey in 2008. He received the M.Sc. department with Signal Processing major from Middle East Technical University, Ankara, Turkey in 2011. He is currently a Ph.D student and researcher in Department of Signal Processing, Tampere University of Technology. He has published 16 papers in international conferences and journals. He has won the the ICPR 2014 Best Student Paper Award for his work in unsupervised salient object detection. His research interests are, visual saliency, semantic segmentation, quantum mechanics based computer vision methods and deep learning.
Selen Pehlivan, TED University
Title: Human activity analysis
Date: June 9, 2016 Thursday
In this talk, I will summarize our work on human activity analysis. First, focusing on multiple camera views, I will describe our method for fusion of frame judgments. We have shown that when there are enough overlapping views to generate a volumetric reconstruction, our recognition performance is comparable with that produced by volumetric reconstructions. Then, I will shortly introduce our recent work on weakly-supervised spatial-temporal action localization.
In the second part of the talk, I will present our work on fine-grained categorization. We introduce a unified approach for classification and keypoint localization, targeting subcategories with wide pose variations and high intra-class similarities. We apply our method to identifying instances of birds.
Finally, I will present our recent effort on action understanding to investigate the representational properties of the Action Observation Network using fMRI and modeling. In our study, we used powerful computational tools from computer vision as well as attribute-based semantic models to represent the videos consisting of natural actions, and linked those models to the brain responses (fMRI) using representational similarity analysis.
Selen Pehlivan holds a B.S. (Bilkent University, 2004) and an M.S. (Koç University, 2006) in Computer Engineering. Between 2004 and 2006, she was a member of the 3DTV EU Project. She got her Ph.D. degree (Bilkent University, 2012) also in Computer Engineering. From August 2009 to February 2011, she was a visiting scholar in Department of Computer Science at University of Illinois at Urbana-Champaign (UIUC). During her graduate studies, she worked as a research and teaching assistant. Before joining TED University, she was a postdoctoral research associate in Computer Vision Group at University of Central Florida (UCF).
Dr. Pehlivan is working on computer vision and machine learning with a primary focus on human activity understanding with video interpretation and object recognition.