Browsing by Author "Ozgur, Atilla"

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Fast Target Detection in Radar Images Using Rayleigh Mixtures And Summed Area Tables
(2018) Nar, Fatih; Okman, Osman Erman; Ozgur, Atilla; Cetin, Mujdat; https://orcid.org/0000-0002-9237-8347; AAD-6546-2019
As the first step of automatic image interpretation systems, automatic detection of targets should be accurate and fast. For Synthetic Aperture Radar (SAR) images, Constant False Alarm Rate (CFAR) is the most popular framework used for target detection. In CFAR, modeling of the clutter is crucial since the decision threshold is calculated based on this model. In this study, we propose to model the background statistics using a Rayleigh Mixture (RM) model. Such an approach facilitates modeling of complex statistics, including but not limited to those involved in heavy tailed distributions, which are shown to be good fits especially for high resolution SAR images. We also propose an efficient method to evaluate CFAR thresholds according to the proposed model by use of Summed Area Tables (SAT). SAT provides a remarkable efficiency as the Rayleigh distribution is represented by only one parameter that can be estimated using simple moments. Tiling and parallel implementation is also utilized for fast computation of results. The outcome is a highly-accurate, extremely fast, and adaptive target detection approach that can be seamlessly used with a variety of complex SAR scenes. Our experiments compare the proposed approach with existing target detection methods and demonstrate its effectiveness as well as the benefits it provides. (C) 2017 Elsevier Inc. All rights reserved.
Feature selection and multiple classifier fusion using genetic algorithms in intrusion detection systems
(2018) Erdem, Hamit; Ozgur, Atilla
With the improvements in information systems, intrusion detection systems (IDS) become more important. IDS can be thought as a classification problem. An important step of classification applications is feature selection step. Nowadays, to improve accuracy of classifiers, it is recommended to use classifier fusion instead of single classifiers. This study proposes to use genetic algorithms for both feature selection and weight selection for classifier fusion in IDS. This proposed system called GA-NS-AB, has been applied to NSL-KDD dataset. Number of classifiers used in fusion changes between 2 and 8. Following classifiers have been used: Adaboost, Decision Tree, Logistic Regression, Naive Bayes, Random Forests, Gradient Boosting, K-Nearest Neighbor, and Neural Networks Multi-Layer Perceptron. The results of the proposed method have been compared with simple voting and probability voting fusion methods and single classifiers. In addition, GA-NS-AB is also compared with previous results. GA-NS-AB is a high accuracy classifier fusion that reduces test and training time.
Journal Finder for TRDIZIN: Baseline Study
(2021) Demirkan, Mert; Ozgur, Atilla; Erdem, Hamit; https://orcid.org/0000-0002-1396-2060; https://orcid.org/0000-0002-9237-8347; https://orcid.org/0000-0003-1704-1581; AAD-6546-2019
One of the main steps in publication of a paper is finding a related journal for the work of the researchers. In the recent years, there have been an increase in scientific papers publications. This situation leads the introduction of journal recommender systems by leading academic publishers. Without using a journal recommender system, this step would be a very time consuming task. This study reviewed similar studies in the literature. Current study is the first version' of journal recommender system for TRDIZIN index which has an increasing amount of articles. A dataset is created by collecting titles, keywords, and abstracts of papers from dergipark web page. Using the collected dataset, a target journal from TRDIZTN is suggested according to title, abstract and keyword of the given article. For the first version of the journal recommender system, cosine similarity is used. The results of the suggested algorithm are evaluated by using performance criteria as the nearest 5 and 10 journals' accuracy.
Parallelization of Sparsity-driven Change Detection Method
(2017) Ozgur, Atilla; Saran, Ayse Nurdan; Nar, Fatih; https://orcid.org/0000-0002-9237-8347; AAD-6546-2019
In this study, Sparsity-driven Change Detection (SDCD) method, which has been proposed for detecting changes in multitemporal synthetic aperture radar (SAR) images, is parallelized to reduce the execution time. Parallelization of the SDCD is realized using OpenMP on CPU and CUDA on GPU. Execution speed of the parallelized SDCD is shown on real-world SAR images. Our experimental results show that the computation time of the parallel implementation brings significant speed-ups.
RmSAT-CFAR: Fast and accurate target detection in radar images
(2018) Nar, Fatih; Okman, Osman Erman; Ozgur, Atilla; Cetin, Mujdat; 0000-0002-9237-8347
As the first step of automatic image interpretation systems, automatic detection of the targets should be accurate and fast. Constant False Alarm Rate (CFAR) is the most popular target detection framework for Synthetic Aperture Radar (SAR) images. For CFAR, modeling of the clutter is crucial since the decision threshold is calculated based on this model. We have developed a new target detection approach in which clutter is modeled using a Rayleigh mixture model that fits well to a variety of high-resolution SAR imagery. For computational efficiency, we use summed area tables (SAT) for computing background statistics. The resulting approach, called RmSAT-CFAR, is a promising general-purpose SAR target detection tool. This paper describes the open-source software for RmSAT-CFAR. Details of RmSAT-CFAR is published in the study named Fast Target Detection in Radar Images using Rayleigh Mixtures and Summed Area Tables. In addition to Rayleigh mixture and SATs, the software also uses tiling and parallelization to obtain faster and scalable results. This software repository also contains open source implementations for following algorithms: (a) Cell Averaging CFAR (CA-CFAR), (b) Automatic Censored CFAR (AC-CFAR), and (c) Adaptive and Fast CFAR (AAF-CFAR). (C) 2017 The Authors. Published by Elsevier B.V.
Sparsity-Driven Change Detection in Multitemporal SAR Images
(2016) Nar, Fatih; Ozgur, Atilla; Saran, Ayse Nurdan; https://orcid.org/0000-0002-9237-8347; AAD-6546-2019
In this letter, a method for detecting changes in multitemporal synthetic aperture radar (SAR) images by minimizing a novel cost function is proposed. This cost function is constructed with log-ratio-based data fidelity terms and an l(1)-norm-based total variation (TV) regularization term. Log-ratio terms model the changes between the two SAR images where the TV regularization term imposes smoothness on these changes in a sparse manner such that fine details are extracted while effects like speckle noise are reduced. The proposed method, sparsity-driven change detection (SDCD), employs accurate approximation techniques for the minimization of the cost function since data fidelity terms are not convex and the employed l(1)-norm TV regularization term is not differentiable. The performance of the SDCD is shown on real-world SAR images obtained from various SAR sensors.
Sparsity-driven weighted ensemble classifier
(2018) Erdem, Hamit; Ozgur, Atilla; Nar, Fatih
In this study, a novel sparsity-driven weighted ensemble classifier (SDWEC) that improves classification accuracy and minimizes the number of classifiers is proposed. Using pre-trained classifiers, an ensemble in which base classifiers votes according to assigned weights is formed. These assigned weights directly affect classifier accuracy. In the proposed method, ensemble weights finding problem is modeled as a cost function with the following terms: (a) a data fidelity term aiming to decrease misclassification rate, (b) a sparsity term aiming to decrease the number of classifiers, and (c) a non-negativity constraint on the weights of the classifiers. As the proposed cost function is non-convex thus hard to solve, convex relaxation techniques and novel approximations are employed to obtain a numerically efficient solution. Sparsity term of cost function allows trade-off between accuracy and testing time when needed. The efficiency of SDWEC was tested on 11 datasets and compared with the state-of-the art classifier ensemble methods. The results show that SDWEC provides better or similar accuracy levels using fewer classifiers and reduces testing time for ensemble.
A Systematic Review of Transfer Learning-Based Approaches for Diabetic Retinopathy Detection
(2023) Oltu, Burcu; Karaca, Busra Kubra; Erdem, Hamit; Ozgur, Atilla; 0000-0002-9237-8347; 0000-0003-1704-1581; AAD-6546-2019
Diabetic retinopathy, which is extreme visual blindness due to diabetes, has become an alarming issue worldwide. Early and accurate detection of DR is necessary to prevent the progression and reduce the risk of blindness. Recently, many approaches for DR detection have been proposed in the literature. Among them, deep neural networks (DNNs), especially Convolutional Neural Network (CNN) models, have become the most offered approach. However, designing and training new CNN architectures from scratch is a troublesome and labor-intensive task, particularly for medical images. Moreover, it requires training tremendous amounts of parameters. Therefore, transfer learning approaches as pre-trained models have become more prevalent in the last few years. Accordingly, in this study, 43 publications based on DNN and Transfer Learning approaches for DR detection between 2016 and 2021 are reviewed. The reviewed papers are summarized in 4 figures and 10 tables that present detailed information about 29 pre-trained CNN models, 13 DR data sets, and standard performance metrics.