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Töreyin, Behçet Uğur

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Profile URL
https://hdl.handle.net/20.500.12416/9315
Name Variants
Töreyin, B.U.
Töreyin, Behçer Uğur
Toreyin, B. Ugur
Toreyin, Behcet Ugur
Job Title
Yrd. Doç. Dr.
Email Address
toreyin@cankaya.edu.tr
Main Affiliation
06.03. Elektrik-Elektronik Mühendisliği
Elektrik-Elektronik Mühendisliği
06. Mühendislik Fakültesi
01. Çankaya Üniversitesi
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Former Staff
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DECENT WORK AND ECONOMIC GROWTH
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GOOD HEALTH AND WELL-BEING
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15

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Scholarly Output

21

Articles

10

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719/14

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WoS Citation Count

190

Scopus Citation Count

258

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7

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8

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WoS Citations per Publication

9.05

Scopus Citations per Publication

12.29

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3

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JournalCount
2012 20th Signal Processing and Communications Applications Conference, SIU 2012, Proceedings -- 2012 20th Signal Processing and Communications Applications Conference, SIU 2012 -- 18 April 2012 through 20 April 2012 -- Fethiye, Mugla -- 907863
2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings -- 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 -- 16 May 2015 through 19 May 2015 -- Malatya -- 1130521
22nd IEEE Signal Processing and Communications Applications Conference (SIU) -- APR 23-25, 2014 -- Karadeniz Teknik Univ, Trabzon, TURKEY1
23nd Signal Processing and Communications Applications Conference (SIU) -- MAY 16-19, 2015 -- Inonu Univ, Malatya, TURKEY1
6th Workshop on Hyperspectral Image and Signal Processing - Evolution in Remote Sensing (WHISPERS) -- JUN 24-27, 2014 -- Lausanne, SWITZERLAND1
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Now showing 1 - 10 of 21
  • Thumbnail Image
    Article
    Citation - WoS: 8
    Citation - Scopus: 10
    Sparse Coding of Hyperspectral Imagery Using Online Learning
    (Springer London Ltd, 2015) Toreyin, Behcet Ugur; Ulku, Irem
    Sparse coding ensures to express the data in terms of a few nonzero dictionary elements. Since the data size is large for hyperspectral imagery, it is reasonable to use sparse coding for compression of hyperspectral images. In this paper, a hyperspectral image compression method is proposed using a discriminative online learning-based sparse coding algorithm. Compression and anomaly detection tests are performed on hyperspectral images from the AVIRIS dataset. Comparative rate-distortion analyses indicate that the proposed method is superior to the state-of-the-art hyperspectral compression techniques.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 6
    Wavelet Based Flame Detection Using Differential Pir Sensors
    (2012) Töreyin, B.U.; Soyer, E.B.; Inaç, I.; Günay, O.; Köse, K.; Çetin, A.E.; Erden, F.
    In this paper, a flame detection system using a differential Pyro-electric Infrared (PIR) sensor is proposed. 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 differential PIR sensor signal is used for feature extraction and feature vectors are fed to Markov models trained with uncontrolled fire flames and walking person. The model yielding the highest probability is chosen. Results suggest that the system can be used in spacious rooms for uncontrolled fire flame detection. © 2012 IEEE.
  • Thumbnail Image
    Article
    Citation - WoS: 10
    Citation - Scopus: 13
    Sparse Representations for Online-Learning Hyperspectral Image Compression
    (Optical Soc Amer, 2015) Toreyin, Behcet Ugur; Ulku, Irem
    Sparse models provide data representations in the fewest possible number of nonzero elements. This inherent characteristic enables sparse models to be utilized for data compression purposes. Hyperspectral data is large in size. In this paper, a framework for sparsity-based hyperspectral image compression methods using online learning is proposed. There are various sparse optimization models. A comparative analysis of sparse representations in terms of their hyperspectral image compression performance is presented. For this purpose, online-learning-based hyperspectral image compression methods are proposed using four different sparse representations. Results indicate that, independent of the sparsity models, online-learning-based hyperspectral data compression schemes yield the best compression performances for data rates of 0.1 and 0.3 bits per sample, compared to other state-of-the-art hyperspectral data compression techniques, in terms of image quality measured as average peak signal-to-noise ratio. (c) 2015 Optical Society of America
  • Thumbnail Image
    Conference Object
    Entropy Functional Based Adaptive Decision Fusion Framework
    (2012) Günay, O.; Töreyin, B.U.; Köse, K.; Çetin, A.E.
    In this paper, an entropy functional based 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 which are updated online according to an active fusion method based on performing entropic 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 was developed to evaluate the performance of the decision fusion algorithm. © 2012 IEEE.
  • Thumbnail Image
    Article
    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
    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.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 2
    Citation - Scopus: 2
    A Two Stage Template Matching Algorithm and Its Implementation on Fpga
    (Institute of Electrical and Electronics Engineers Inc., 2015) Sever, R.; Töreyin, B.U.; Aktaş, H.
    In this paper, to decrease the computational cost and number of cycles in Template Matching Algorithm, a novel two-stage algorithm is proposed. The Sum of Absolute Differences method is used for matching. The proposed algorithm is implemented on Field-Programmable-Gate-Array (FPGA). The algorithm is accelerated with the effective usage of Block RAMs distributed on FPGA. Thus, the proposed algorithm became fast enough for real time object tracking applications on UAVs. © 2015 IEEE.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 2
    Citation - Scopus: 13
    Lossless Hyperspectral Image Compression Using Wavelet Transform Based Spectral Decorrelation
    (Ieee, 2015) Yilmaz, Ozan; Mert, Yakup Murat; Turk, Fethi; Toreyin, Behcet Ugur
    Integer-coefficient Discrete Wavelet Transformation (DWT) filters widely used in the literature are implemented and investigated as spectral decorrelator. As the performance of spectral decorrelation step has direct impact on the compression ratio (CR), it is important to employ the most convenient spectral decorrelator in terms of computational complexity and CR. Tests using AVIRIS image data set are carried out and CRs corresponding to various subband decomposition levels are presented within a lossless hyperspectral compression framework. Two-dimensional images corresponding to each band is compressed using JPEG-LS algorithm. Results suggest that Cohen-Daubechies-Feauveau (CDF) 9/7 integer-coefficient wavelet transform with five levels of spectral subband decomposition would be an efficient spectral decorrelator for on-board lossless hyperspectral image compression.
  • Thumbnail Image
    Article
    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
    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.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 1
    Citation - Scopus: 1
    Graph-Cut Compression Algorithm for Compressed-Sensed Image Acquisition
    (Ieee, 2014) Gulen, Seden Hazal; Trocan, Maria; Toreyin, Behcet Ugur; Alaydin, Julide Gulen
    The purpose of the paper is to find the best quantizer allocation for compressed-sensed acquired images, by using a graph-cut quantizer allocation method. The compressed sensed acquisition is realized in a block-based manner, using a random projection matrix, and on the obtained block measurements a graph-cut-based quantizer allocation method is applied, in order to further reduce the bitrate associated to the measurements. Finally, the quantized measurements are reconstructed using a Smooth Projected Landweber recovery method. The proposed compression method for compressed sensed acquisition shows better results when compared to JPEG2000.
  • Thumbnail Image
    Article
    Citation - WoS: 35
    Citation - Scopus: 46
    Fall Detection Using Single-Tree Complex Wavelet Transform
    (Elsevier, 2013) Keskin, Furkan; Toreyin, B. Ugur; Cetin, A. Enis; Yazar, Ahmet
    The goal of Ambient Assisted Living (AAL) research is to improve the quality of life of the elderly and handicapped people and help them maintain an independent lifestyle with the use of sensors, signal processing and telecommunications infrastructure. Unusual human activity detection such as fall detection has important applications. In this paper, a fall detection algorithm for a low cost AAL system using vibration and passive infrared (PIR) sensors is proposed. The single-tree complex wavelet transform (ST-CWT) is used for feature extraction from vibration sensor signal. The proposed feature extraction scheme is compared to discrete Fourier transform and mel-frequency cepstrum coefficients based feature extraction methods. Vibration signal features are classified into "fall" and "ordinary activity" classes using Euclidean distance, Mahalanobis distance, and support vector machine (SVM) classifiers, and they are compared to each other. The PIR sensor is used for the detection of a moving person in a region of interest. The proposed system works in real-time on a standard personal computer. (C) 2012 Elsevier B.V. All rights reserved.