Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Novel Methods for Depth-Based Calibration of Multiple RGBD Cameras Using Four Mutually Equidistant Spheres(IEEE-Inst Electrical Electronics Engineers Inc, 2025) CalI, Esra Tuncer; Gumustekin, SevketThis article presents novel calibration methods specifically tailored for multiple depth cameras, utilizing solely depth images. Traditional approaches often rely on infrared (IR) images of checkerboards, which, while feasible, fail to exploit the measured depth values, leading to calibration inaccuracies and 3-D misregistration errors. To overcome this limitation, we designed a 3-D tetrahedron object comprising four spheres placed at each corner. By employing an ellipse-fitting technique, we accurately identified the sphere centers in the depth images. Using these centers, we utilized 3-D reprojection errors and measured depths within a bundle adjustment framework to jointly determine the calibration parameters for four depth cameras. Our proposed methods significantly reduce error values compared with those obtained using IR images of checkerboards. The versatility of our techniques ensures their applicability to various types of depth cameras, independent of their underlying technologies. Here, we demonstrate that by integrating depth information directly into the calibration process, we achieve remarkable improvements. Our first method reduces the average system reconstruction error by 78.98%, while our second method, which introduces a novel cost function tailored to the tetrahedron object, achieves an even more substantial reduction of 82.32%. These results underscore the superiority of our depth-integrated calibration approach, particularly in the context of 3-D reconstruction involving multiple depth cameras.Book Part Citation - Scopus: 3Real-Time Flood Hydrograph Predictions Using Rating Curve and Soft Computing Methods (ga, Ann)(Elsevier, 2022) Tayfur, GökmenThis chapter introduces hydraulic and hydrologic flood routing methods in natural channels. It details hydrological flood routing methods of the Rating Curve and Muskingum. Based on the rating curve method (RCM), it presents real-time flood hydrograph predictions using the genetic algorithm (GA-based RCM) model. In addition, it presents how to make real-time flood hydrograph predictions using the artificial neural network (ANN). The chapter briefly introduces the basics of GA and details how to calibrate and validate the GA-based RCM model using measured real-time flood hydrographs. Similarly, after giving the basics of ANN, it shows how to train and test the ANN model using measured hydrographs. Real hydrograph simulations by the RCM, GA-based RCM, and ANN are presented, and merits of each model are discussed. © 2023 Elsevier Inc. All rights reserved.Article Citation - WoS: 9Citation - Scopus: 8Quantitative Determination of Phenolic Compounds in Propolis Samples From the Black Sea Region (türkiye) Based on Hptlc Images Using Partial Least Squares and Genetic Inverse Least Squares Methods(Elsevier, 2023) Güzelmeriç, Etil; Özdemir, Durmuş; Şen, Nisa Beril; Çelik, Cansel; Yeşilada, ErdemThe complex chemical composition of propolis is related to the plant source to be used by honeybees. Propolis type is defined based on the plant source with the highest proportion in its composition, which is determined by chromatographic techniques as high-performance thin-layer chromatography (HPTLC). In addition to marker component identification to specify the propolis type, quantification of its proportion is also significant for prediction and reproducible pharmacological activity. One drawback for propolis marker component quantita-tion is that during the chromatographical analysis, not the main but the other plant sources with less proportion may cause interferences during the chemical analysis. In this study, the amounts of marker components were compared with the reference analysis data obtained by high-performance liquid chromatography (HPLC) and from HPTLC images using Partial Least Squares (PLS) and Genetic Inverse Least Squares (GILS) regression methods. Firstly, HPTLC images of propolis samples were processed by an image algorithm (developed in MATLAB) where the bands of each standard and the samples were cut same dimensional pieces as 351 x 26 pixels in height and width, respectively. Simultaneously, reference analysis of the marker components in propolis samples was performed with a validated HPLC method. Consequently, the reference values obtained from HPLC versus PLS, and GILS predicted values of the eight compounds based on the digitized HPTLC images of the chromatograms were found to be matched successfully. The results of the multivariate calibration models demonstrated that HPTLC images could be used quantitatively for quality control of propolis used as a food supplement.