Browsing by Author "Kose, Kivanc"

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Citation - WoS: 72
Citation - Scopus: 85
Entropy-Functional Online Adaptive Decision Fusion Framework With Application To Wildfire Detection in Video
(Ieee-inst Electrical Electronics Engineers inc, 2012) Toreyin, Behcet Ugur; Kose, Kivanc; Cetin, A. Enis; Gunay, Osman; 19325; 01. Çankaya Üniversitesi; 06. Mühendislik Fakültesi; 06.03. Elektrik-Elektronik Mühendisliği
In this paper, an entropy-functional-based online adaptive decision fusion (EADF) framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several subalgorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular subalgorithm. Decision values are linearly combined with weights that are updated online according to an active fusion method based on performing entropic projections onto convex sets describing subalgorithms. It is assumed that there is an oracle, who is usually a human operator, providing feedback to the decision fusion method. A video-based wildfire detection system was developed to evaluate the performance of the decision fusion algorithm. In this case, image data arrive sequentially, and the oracle is the security guard of the forest lookout tower, verifying the decision of the combined algorithm. The simulation results are presented.
Citation - WoS: 34
Citation - Scopus: 39
Wavelet Based Flickering Flame Detector Using Differential Pir Sensors
(Elsevier Sci Ltd, 2012) Toreyin, B. Ugur; Soyer, E. Birey; Inac, Ihsan; Gunay, Osman; Kose, Kivanc; Cetin, A. Enis; Erden, Fatih; 19325; 2147; 243050; 06.03. Elektrik-Elektronik Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
A Pyro-electric Infrared (FIR) sensor based flame detection system is proposed using a Markovian decision algorithm. A differential PIR sensor is only sensitive to sudden temperature variations within its viewing range and it produces a time-varying signal. The wavelet transform of the FIR sensor signal is used for feature extraction from sensor signal and wavelet parameters are fed to a set of Markov models corresponding to the flame flicker process of an uncontrolled fire, ordinary activity of human beings and other objects. The final decision is reached based on the model yielding the highest probability among others. Comparative results show that the system can be used for fire detection in large rooms. (c) 2012 Elsevier Ltd. All rights reserved.