Video shot boundary detection by graph theoretic approaches

Download
2008
Aşan, Emrah
This thesis aims comparative analysis of the state of the art shot boundary detection algorithms. The major methods that have been used for shot boundary detection such as pixel intensity based, histogram-based, edge-based, and motion vectors based, are implemented and analyzed. A recent method which utilizes “graph partition model” together with the support vector machine classifier as a shot boundary detection algorithm is also implemented and analyzed. Moreover, a novel graph theoretic concept, “dominant sets”, is also successfully applied to the shot boundary detection problem as a contribution to the solution domain.

Suggestions

Video Shot Boundary Detection by Graph-theoretic Dominant Sets Approach
Asan, Emrah; Alatan, Abdullah Aydın (2009-09-16)
We present a video shot boundary detection algorithm based on the novel graph theoretic concept, namely dominant sets. Dominant sets are defined as a set of the nodes in a graph, mostly similar to each other and dissimilar to the others. In order to achieve this goal, candidate shot boundaries are determined by using simply pixelwise differences between consequent frames. For each candidate position, a testing sequence is constructed by considering 4 frames before the candidate position and 2 frames after t...
Dim target detection in infrared imagery
Çifçi, Barış; Atalay, Aydın; Department of Electrical and Electronics Engineering (2006)
This thesis examines the performance of some dim target detection algorithms in low-SNR imaging scenarios. In the past research, there have been numerous attempts for detection and tracking barely visible targets for military surveillance applications with infrared sensors. In this work, two of these algorithms are analyzed via extensive simulations. In one of these approaches, dynamic programming is exploited to coherently integrate the visible energy of dim targets over possible relative directions, where...
Higher order levelable mrf energy minimization via graph cuts
Karcı, Mehmet Haydar; Demirekler, Mübeccel; Department of Electrical and Electronics Engineering (2008)
A feature of minimizing images of a class of binary Markov random field energies is introduced and proved. Using this, the collection of minimizing images of levels of higher order, levelable MRF energies is shown to be a monotone collection. This implies that these images can be combined to give minimizing images of the MRF energy itself. Due to the recent developments, second and third order binary MRF energies of the mentioned class are known to be exactly minimized by maximum flow/minimum cut computatio...
SystemC implementation with analog mixed signal modeling for a microcontroller
Mert, Yakup Murat; Aşkar, Murat; Department of Electrical and Electronics Engineering (2007)
In this thesis, an 8-bit microcontroller, PIC 16F871, has been implemented using SystemC with classical hardware design methods. Analog modules of the microcontroller have been modeled behaviorally with SystemC-AMS which is the analog and mixed signal extensions for the SystemC. SystemC-AMS provides the capability to model non-digital modules and synchronization with the SystemC kernel. In this manner, electronic systems that have both digital and analog components can be described and simulated very effect...
Reinforcement learning using potential field for role assignment in a multi-robot two-team game
Fidan, Özgül; Erkmen, İsmet; Department of Electrical and Electronics Engineering (2004)
In this work, reinforcement learning algorithms are studied with the help of potential field methods, using robosoccer simulators as test beds. Reinforcement Learning (RL) is a framework for general problem solving where an agent can learn through experience. The soccer game is selected as the problem domain a way of experimenting multi-agent team behaviors because of its popularity and complexity.
Citation Formats
E. Aşan, “Video shot boundary detection by graph theoretic approaches,” M.S. - Master of Science, Middle East Technical University, 2008.