Çankaya GCRIS Standart veritabanının içerik oluşturulması ve kurulumu Research Ecosystems (https://www.researchecosystems.com) tarafından devam etmektedir. Bu süreçte gördüğünüz verilerde eksikler olabilir.
 

Töreyin, Behçet Uğur

Profile Picture
Name Variants
Job Title
Yrd. Doç. Dr.
Email Address
toreyin@cankaya.edu.tr
Main Affiliation
Elektrik-Elektronik Mühendisliği
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

Sustainable Development Goals Report Points

SDG data could not be loaded because of an error. Please refresh the page or try again later.
Scholarly Output

17

Articles

18

Citation Count

503

Supervised Theses

0

Filters

2012 - 201817
Reset filters

Settings

Scholarly Output Search Results

Now showing 1 - 10 of 17
  • Thumbnail Image
    Conference Object
    Citation - WoS: 0
    Citation - Scopus: 0
    Evaluation of Clustering Performance of Hyperspectral Bands
    (Ieee, 2015) Haliloglu, Onur; Töreyin, Behçet Uğur; Sakarya, Ufuk; Toreyin, Behcet Ugur; 19325; Elektrik-Elektronik Mühendisliği
    Hyperspectral images have huge data volume that contains spectral and spatial information. This high data volume leads to processing, storage, and transmission problems. Moreover, insufficient training data results in Hughes phenomenon. It is possible to solve these problems with the help of feature selection. In this paper, a method that evaluates the clustering performance of spectral bands is proposed as a pre-processing operation in order to realize feature selection. This method is clustering each spectral band based on "dominant sets" technique and it evaluates the clustering performance of each band. The proposed method is time efficient since it works on a small set of training data instead of the whole hyperspectral data. In this study, "dominant sets" technique is first applied to hyperspectral image processing as a clustering method.
  • 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) Aktaş, H.; Töreyin, Behçet Uğur; Sever, R.; Töreyin, B.U.; 19325; Elektrik-Elektronik Mühendisliği
    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: 1
    Citation - Scopus: 1
    Graph-Cut-based Compression Algorithm for Compressed-Sensed Image Acquisition
    (Ieee, 2014) Alaydin, Julide Gulen; Töreyin, Behçet Uğur; Gulen, Seden Hazal; Trocan, Maria; Toreyin, Behcet Ugur; 19325; Elektrik-Elektronik Mühendisliği
    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
    Conference Object
    Citation - WoS: 2
    Citation - Scopus: 12
    Lossless Hyperspectral Image Compression Using Wavelet Transform Based Spectral Decorrelation
    (Ieee, 2015) Toreyin, Behcet Ugur; Töreyin, Behçet Uğur; Yilmaz, Ozan; Mert, Yakup Murat; Turk, Fethi; 19325; Elektrik-Elektronik Mühendisliği
    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: 8
    Citation - Scopus: 9
    Joint parameter and state estimation of the hemodynamic model by iterative extended Kalman smoother
    (Elsevier Sci Ltd, 2016) Aslan, Serdar; Töreyin, Behçet Uğur; Cemgil, Ali Taylan; Aslan, Murat Samil; Toreyin, Behcet Ugur; Akin, Ata; 19325; Elektrik-Elektronik Mühendisliği
    The joint estimation of the parameters and the states of the hemodynamic model from the blood oxygen level dependent (BOLD) signal is a challenging problem. In the functional magnetic resonance imaging (fMRI) literature, quite interestingly, many proposed algorithms work only as a filtering method. This makes the estimation of hidden states and parameters less reliable compared with the algorithms that use smoothing. In standard implementations, smoothing is performed only once. However, joint state and parameter estimation can be improved substantially by iterating smoothing schemes such as the extended Kalman smoother (IEKS). In the fMRI literature, extended Kalman filtering is thought to be less accurate than standard particle filtering (PF). We compared EKF with PF and observed that the contrary is true. We improved the EKF performance by adding smoother. By iterative scheme joint hemodynamic and parameter estimation is improved substantially. We compared IEKS performance with the square-root cubature Kalman smoother (SCKS) algorithm. We show that its accuracy for the state and the parameter estimation is better and much faster than iterative SCKS. SCKS was found to be a better estimator than the dynamic expectation maximization (DEM), EKF, local linearization filter (LLF) and PP methods. We show in this paper that IEKS is a better estimator than iterative SCKS under different process and measurement noise conditions. As a result, IEKS seems to be the best method we evaluated in all aspects. (C) 2015 Elsevier Ltd. All rights reserved.
  • Thumbnail Image
    Article
    Citation - WoS: 8
    Citation - Scopus: 10
    Sparse coding of hyperspectral imagery using online learning
    (Springer London Ltd, 2015) Ulku, Irem; Töreyin, Behçet Uğur; Toreyin, Behcet Ugur; 17575; 19325; Elektrik-Elektronik Mühendisliği
    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
    Article
    Citation - WoS: 204
    Citation - Scopus: 267
    Video fire detection - Review
    (Academic Press inc Elsevier Science, 2013) Cetin, A. Enis; Töreyin, Behçet Uğur; Dimitropoulos, Kosmas; Gouverneur, Benedict; Grammalidis, Nikos; Gunay, Osman; Habiboglu, Y. Hakan; Verstockt, Steven; 19325; 2147; Elektrik-Elektronik Mühendisliği
    This is a review article describing the recent developments in Video based Fire Detection (VFD). Video surveillance cameras and computer vision methods are widely used in many security applications. It is also possible to use security cameras and special purpose infrared surveillance cameras for fire detection. This requires intelligent video processing techniques for detection and analysis of uncontrolled fire behavior. VFD may help reduce the detection time compared to the currently available sensors in both indoors and outdoors because cameras can monitor "volumes" and do not have transport delay that the traditional "point" sensors suffer from. It is possible to cover an area of 100 km(2) using a single pan-tilt-zoom camera placed on a hilltop for wildfire detection. Another benefit of the VFD systems is that they can provide crucial information about the size and growth of the fire, direction of smoke propagation. (c) 2013 Elsevier Inc. All rights reserved.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 3
    Citation - Scopus: 3
    Image Analysis Based Fish Tail Beat Frequency Estimation for Fishway Efficiency
    (Ieee Computer Soc, 2018) Töreyin, Behçet Uğur; Yildirim, Yasin; Toreyin, B. Ugur; Küçükali, Serhat; Kucukali, Serhat; Verep, Bulent; Turan, Davut; Alp, Ahmet; 20413; Elektrik-Elektronik Mühendisliği; İnşaat Mühendisliği
    In this paper, we propose image analysis based methods for estimating fish tail beat frequency, which is an indicator of fish energy consumption at fish passage structures. For this purpose, average magnitude difference and autocorrelation function based periodicity detection techniques are utilized. Actual fish images are acquired using a visible range camera installed in a brush type fish pass in Ikizdere River, near Rize, Turkey, which is very rich in biodiversity. Results show that image analysis based periodicity detection methods can be used for fishway efficiency evaluation purposes. To the best of authors' knowledge, this is the first study that automatically estimates fish tail beat frequency using image analysis. The findings of this study are expected to have implications for fish monitoring and fishway design.
  • Thumbnail Image
    Article
    Citation - WoS: 71
    Citation - Scopus: 82
    Entropy-Functional-Based Online Adaptive Decision Fusion Framework With Application to Wildfire Detection in Video
    (Ieee-inst Electrical Electronics Engineers inc, 2012) Gunay, Osman; Töreyin, Behçet Uğur; Toreyin, Behcet Ugur; Kose, Kivanc; Cetin, A. Enis; 19325; 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.
  • Thumbnail Image
    Article
    Citation - WoS: 10
    Citation - Scopus: 12
    Sparse representations for online-learning-based hyperspectral image compression
    (Optical Soc Amer, 2015) Ulku, Irem; Töreyin, Behçet Uğur; Toreyin, Behcet Ugur; 17575; 19325; Elektrik-Elektronik Mühendisliği
    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