Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Regression Via Classification for Fingerprint Orientation Estimation(Ieee-inst Electrical Electronics Engineers inc, 2024) Erdogmus, NesliEstimating the direction in which the ridges and valleys of the fingerprint pattern are aligned often serves as a pivotal first step in fingerprint recognition systems. The ridge orientation map is a fundamental reference for subsequent processing stages, such as image enhancement, feature extraction, and matching. Therefore, its accuracy is essential to achieve high recognition rates. Ridge orientation estimation entails a regression problem since the task is to estimate an angle between 0 degrees and 180 degrees for each sub-region in the fingerprint image. However, the majority of the approaches in the literature pivot towards framing this regression task as a classification problem. This paper systematically analyzes the regression via classification methodology for fingerprint orientation estimation, exploring various discretization and encoding strategies. Specifically, we examine single and multiple discretization schemes designed to ensure that resulting bins maintain uniform length or uniform probability or are allocated randomly, paired with one-hot, ordinal, and cyclic encoding techniques. Our experiments are conducted on the FOE-TEST database from FVC-onGoing, the sole publicly available fingerprint orientation dataset. The findings highlight the efficacy of cyclic encoding over the one-hot encoding prevalent in prior research, while equal-length and equal-probability discretization strategies yield comparable results.Article Citation - WoS: 6Citation - Scopus: 8Software Size Measurement: Bridging Research and Practice(IEEE Computer Society, 2024) Hacaloglu,T.; Unlu,H.; Yildiz,A.; Demirors,O.Despite the availability of software size measures with proven effectiveness, structured characteristics, and reliability, practitioners often favor subjective estimation approaches like story points due to perceived ease and flexibility. Amid ongoing industry transformations driven by artificial intelligence, distributed architectures, and agile practices, innovative approaches to software size measurement are crucial to aligning research solutions with evolving industry demands. This study investigates the limited adoption of functional size measurement methods in the software development industry despite their research-backed success. By gathering insights from firms experienced in size measurement, the research aims to uncover industry expectations and facilitate the translation of theoretical methodologies into practical applications. This effort seeks to overcome barriers and promote the integration of novel concepts into the software development landscape. IEEEArticle Citation - WoS: 2Citation - Scopus: 2Estimating Probability Density Functions and Entropies of Chua's Circuit Using B-Spline Functions(World Scientific Publishing Co. Pte Ltd, 2012) Savacı, Ferit Acar; Güngör, Mesutn this paper, first the probability density functions (PDFs) of the states of Chua's circuit have been estimated using B-spline functions and then the state entropies of Chua's circuit with respect to the bifurcation parameter have been obtained. The results of the proposed B-spline density estimator have been compared with the results obtained from the Parzen density estimator. © 2012 World Scientific Publishing Company.Article Citation - WoS: 1Citation - Scopus: 3Euclidean Position Estimation of Static Features Using a Moving Uncalibrated Camera(Institute of Electrical and Electronics Engineers Inc., 2012) Nath, Nitendra; Dawson, Darren M.; Tatlıcıoğlu, EnverIn this paper, a novel Euclidean position estimation technique using a single uncalibrated camera mounted on amoving platform is developed to asymptotically recover the 3-D Euclidean position of static object features. The position of the moving platform is assumed to be measurable, and a second object with known 3-D Euclidean coordinates relative to theworld frame is considered to be available a priori. To account for the unknown camera calibration parameters and to estimate the unknown 3-D Euclidean coordinates, an adaptive least squares estimation strategy is employed based on prediction error formulations and a Lyapunovtype stability analysis. The developed estimator is shown to recover the 3-D Euclidean position of the unknown object features despite the lack of knowledge of the camera calibration parameters. Numerical simulation results along with experimental results are presented to illustrate the effectiveness of the proposed algorithm. © 2011 IEEE.Conference Object Citation - Scopus: 1Euclidean Position Estimation of Static Features Using a Moving Uncalibrated Camera(Institute of Electrical and Electronics Engineers Inc., 2009) Nath, Nitendra; Dawson, Darren M.; Tatlıcıoğlu, EnverIn this paper, a novel Euclidean position estimation technique using a single uncalibrated camera mounted on a moving platform is developed to asymptotically recover the three-dimensional (3D) Euclidean position of static object features. The position of the moving platform is assumed to be measurable, and a second object with known 3D Euclidean coordinates relative to the world frame is considered to be available a priori. To account for the unknown camera calibration parameters and to estimate the unknown 3D Euclidean coordinates, an adaptive least squares estimation strategy is employed based on prediction error formulations and a Lyapunov-type stability analysis. The developed estimator is proven to recover the 3D Euclidean position of the unknown object features despite the lack of knowledge of the camera calibration parameters.