Browsing by Author "Gunay, O."

Now showing 1 - 5 of 5

Open Access
Compressive sensing using the modified entropy functional
(Academic Press, 2014-01) Kose, K.; Gunay, O.; Çetin, A. Enis
In most compressive sensing problems, 1 norm is used during the signal reconstruction process. In this article, a modified version of the entropy functional is proposed to approximate the 1 norm. The proposed modified version of the entropy functional is continuous, differentiable and convex. Therefore, it is possible to construct globally convergent iterative algorithms using Bregman’s row-action method for compressive sensing applications. Simulation examples with both 1D signals and images are presented. © 2013 Elsevier Inc. All rights reserved.
Open Access
Entropy-functional-based online adaptive decision fusion framework with application to wildfire detection in video
(IEEE, 2012-01-09) Gunay, O.; Toreyin, B. U.; Kose, K.; Çetin, A. Enis
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.
Open Access
Online adaptive decision fusion framework based on projections onto convex sets with application to wildfire detection in video
(S P I E - International Society for Optical Engineering, 2011-07-06) Gunay, O.; Toreyin, B. U.; Çetin, A. Enis
In this paper, an online adaptive decision fusion framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several sub-algorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular sub-algorithm. Decision values are linearly combined with weights that are updated online according to an active fusion method based on performing orthogonal projections onto convex sets describing sub-algorithms. 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 is developed to evaluate the performance of the algorithm in handling the problems where data arrives sequentially. In this case, the oracle is the security guard of the forest lookout tower verifying the decision of the combined algorithm. Simulation results are presented.
Open Access
Video-Based FLame detection for the protection of cultural heritage
(SAGE, 2013) Dimitropoulos, K.; Gunay, O.; Kose, K.; Erden, F.; Chaabane, F.; Tsalakanidou, F.; Grammalidis, N.; Çetin, A. Enis
The majority of cultural heritage and archaeological sites, especiallyin the Mediterranean region, are covered with vegetation, whichincreases the risk of fires. These fires may also break out and spreadtowards nearby forests and other wooded land, or conversely start innearby forests and spread to archaeological sites. Beyond takingprecautionary measures to avoid a forest fire, early warning andimmediate response to a fire breakout are the only ways to avoidgreat losses and environmental and cultural heritage damages. Theuse of terrestrial systems, typically based on video cameras, iscurrently the most promising solution for advanced automatic wildfiresurveillance and monitoring due to its low cost and short responsetime. Early and accurate detection and localization of flame is anessential requirement of these systems, however, it remains achallenging issue due to the fact that many natural objects havesimilar characteristics with fire. This paper presents and comparesthree video-based flame detection techniques, which weredeveloped within the FIRESENSE EU research project, taking intoaccount the chaotic and complex nature of the fire phenomenonand the large variations of flame appearance in video. Experimentalresults show that the proposed methods provide high fire detectionrates with reasonable false alarm ratios.
Open Access
Wavelet based flickering flame detector using differential PIR sensors
(Elsevier, 2012-07-06) Erden, F.; Toreyin, B. U.; Soyer, E. B.; Inac, I.; Gunay, O.; Kose, K.; Çetin, A. Enis
A Pyro-electric Infrared (PIR) 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 PIR 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.