Elektrik Elektronik Mühendisliği Bölümü

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Citation Count: Toreyin, Behcet Ugur; Aktas, Hakan; Sever, Refik, "A Two Stage Template Matching Algorithm and Its Implementation on FPGA", 23nd Signal Processing and Communications Applications Conference (SIU), pp. 2214-2217, (2015).
A Two Stage Template Matching Algorithm and Its Implementation on FPGA
(IEEE, 2015) Aktaş, Hakan; Sever, Refik; Töreyin, Behçet Uğur; 19325
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.
Evaluation of Clustering Performance of Hyperspectral Bands
(IEEE, 2015) Haliloğlu, Onur; Sakarya, Ufuk; Töreyin, Behçet Uğur; 19325
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.
Citation Count: Töreyin, Behçet Uğur; Yılmaz, Ozan; Mert, Yakup Murat. "Evaluation of on-board integer wavelet transform based spectral decorrelation schemes for lossless compression of hyperspectral images", 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Lausanne: Switzerland, 2014.
Evaluation of on-board integer wavelet transform based spectral decorrelation schemes for lossless compression of hyperspectral images
(2014) Töreyin, Behçet Uğur; Yılmaz, Ozan; Mert, Yakup Murat; 19325
Integer-coefficient Discrete Wavelet Transformation (DWT) filters widely used in the literature are implemented and investigated as spectral decorrelator for on-board lossless hyperspectral image compression. 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 low computational complexity and high CR. Extensive 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. Results suggest that Cohen-DaubechiesFeauveau (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.
Graph-Cut-based Compression Algorithm for Compressed-Sensed Image Acquisition
(IEEE, 2014) Alaydin, Julide Gulen; Gülen, Seden Hazal; Trocan, Maria; Töreyin, Behçet Uğur; 19325
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.
Citation Count: Ülkü, İrem; Töreyin, B. Uğur (2015). "Hyperspectral image compression using an online learning method", Proceedings of SPIE - The International Society for Optical Engineering, Vol. 9501.
Hyperspectral image compression using an online learning method
(2015) Ülkü, İrem; Töreyin, B. Uğur; 19325
A hyperspectral image compression method is proposed using an online dictionary learning approach. The online learning mechanism is aimed at utilizing least number of dictionary elements for each hyperspectral image under consideration. In order to meet this "sparsity constraint", basis pursuit algorithm is used. Hyperspectral imagery from AVIRIS datasets are used for testing purposes. Effects of non-zero dictionary elements on the compression performance are analyzed. Results indicate that, the proposed online dictionary learning algorithm may be utilized for higher data rates, as it performs better in terms of PSNR values, as compared with the state-of-the-art predictive lossy compression schemes. © 2015 SPIE.
Citation Count: Aslan, Serdar...et al., "Joint parameter and state estimation of the hemodynamic model by iterative extended Kalman smoother", Biomedical Signal Processing and Control, Vol. 24, pp. 47-62, (2016).
Joint parameter and state estimation of the hemodynamic model by iterative extended Kalman smoother
(Elsevier SCI LTD, 2016) Aslan, Serdar; Cemgil, Ali Taylan; Aslan, Murat Samil; Töreyin, Behçet Uğur; Akın, Ata; 19325
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.
Citation Count: Toreyin, Behcet Ugur...et al., "Lossless Hyperspectral Image Compression Using Wavelet Transform Based Spectral Decorrelation", 7th International Conference on Recent Advances in Space Technologies (RAST), pp. 250-253, (2015).
Lossless Hyperspectral Image Compression Using Wavelet Transform Based Spectral Decorrelation
(IEEE, 2015) Töreyin, Behçet Uğur; Yılmaz, Ozan; Mert, Yakup Murat; Türk, Fethi; 19325
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.
Citation Count: Ülkü, İ., Töreyin, B.U. (2015). Sparse coding of hyperspectral imagery using online learning. Signal Image And Video Processing, 9(4), 959-966. http://dx.doi.org/10.1007/s11760-015-0753-9
Sparse coding of hyperspectral imagery using online learning
(Springer, 2015) Ülkü, İrem; Töreyin, Behçet Uğur; 17575; 19325
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.
Citation Count: Ülkü, İ., Töreyin, B.U. (2015). Sparse representations for online-learning-based hyperspectral image compression. Applied Optics, 54(29), 8625-8631. http://dx.doi.org/ 10.1364/AO.54.008625
Sparse representations for online-learning-based hyperspectral image compression
(Optical Soc Amer, 2015) Ülkü, İrem; Töreyin, Behçet Uğur; 17575; 19325
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.
Citation Count: Erden, F...et al. "Wavelet based flame detection using differential PIR sensors", 20th Signal Processing and Communications Applications Conference, SIU 2012, pp. 1-4, 2012.
Wavelet based flame detection using differential PIR sensors
(IEEE, 2012) Erden, F.; Töreyin, Behçet Uğur; Soyer, E. B.; İnaç, İ.; Günay, O.; Köse, K.; Çetin, A. Enis; 19325
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.

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