Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2025A Video-Based Mobile Palmprint Dataset and an Illumination-Robust Deep Learning Architecture for Unconstrained Environments
Koşmaz Sünnetci, Betül; Bingöl, Özkan; Gedikli, Eyüp; Ekinci, Murat; Doğan, Ramazan Özgür; Türk, Salih; Güngör, Nihan
Featured Application: The proposed framework facilitates secure and user-friendly biometric authentication on mobile platforms, particularly in mobile payment and access control scenarios, under unconstrained conditions. The widespread adoption of mobile devices has made secure and user-friendly biometric authentication critical. However, widely used modalities such as fingerprint and facial recognition show limited robustness under uncontrolled illumination and on heterogeneous devices. In contrast, palmprint recognition offers strong potential because of its rich textural patterns and high discriminative power. This study addresses the limitations of laboratory-based datasets that fail to capture real-world challenges. We introduce MPW-180, a novel dataset comprising videos of 180 participants recorded on their own smartphones in everyday environments. By systematically incorporating diverse illumination conditions (with and without flash) and natural free-hand movements, MPW-180 is the first dataset to adopt a bring-your-own-device paradigm, providing a realistic benchmark for evaluating generalization in mobile biometric models. In addition, we propose PalmWildNet, an SE-block-enhanced deep learning architecture trained with Triplet Loss and a cross-illumination sampling strategy. The experimental results show that conventional methods suffer over 50% performance degradation under cross-illumination conditions. In contrast, our method reduces the Equal Error Rate to 1–2% while maintaining an accuracy above 97%. These findings demonstrate that the proposed framework not only tolerates illumination variability but also learns robust illumination-invariant representations, making it well-suited for mobile biometric authentication.
- BildiriYalnız künye2025Depth Estimation Based Automatic Pose Correction in Hand Images
Sünnetci Koşmaz, Betül; Bingöl, Özkan; Gedikli, Eyüp; Ekinci, Murat
In palm biometric recognition systems, the hand being at different angles and positions relative to the camera presents a critical challenge in terms of accuracy and reliability. Especially in images captured in free environments, hand misalignment can negatively affect recognition performance by making it difficult to extract biometric attributes. Perspective distortions due to camera position change the geometric structure of the hand, compromising biometric data integrity. While 3D imaging systems can offer successful solutions for pose normalization, performing a similar transformation for 2D images poses a major technical challenge. In this study, depth estimation supported plane fitting is used to remove perspective distortions in 2D images and pose normalization of the hand is obtained. The proposed method uses the information obtained from depth estimation to realign the hand image as if it were standing directly in front of the camera, improving geometric consistency. This will make the extraction of biometric features more reliable and contribute to improving the accuracy of palm recognition systems.
- MakaleAçık Erişim2023Detection of pneumonia from pediatric chest X-ray images by transfer learning
Demir, Yasin; Bingöl, Özkan
When pathogens such as viruses, bacteria and fungi attack the lungs, the alveoli fill with inflamed fluid, causing pneumonia. Early diagnosis of this disease, which has fatal outcomes especially in children under 5 years old, is very important in controlling undesirable situations. Chest X-ray images play an important role in the diagnosis of pneumonia. In addition, the fact that the amount of radiation is lower than imaging devices such as tomography and the possibility of being accessible even from rural areas creates an advantage for X-ray devices. However, X-ray images that are not always clear or human conditions such as fatigue and lack of attention can make it difficult for specialists to detect pneumonia. In this study, a transfer learning-based convolutional neural network (CNN) approach is proposed, which can help specialists in the early and accurate diagnosis of pneumonia in children and, classify healthy and diseased individuals through Chest X-ray images. As a result of the study, an original CNN was proposed by adding additional layers to the AlexNet architecture layers and a test accuracy of 96.31% was obtained. Copyright 2021, Yıldız Technical University.
- MakaleYalnız künye2017Stereo-based palmprint recognition in various 3D postures
Bingöl, Özkan; Ekinci, Murat
In order to increase performance in palmprint recognition systems, various devices are normally used to restrict the movement of the hand. These can cause problems, especially for those users with physical disabilities. They also cause significant hygiene problems in multi-user systems. Recently, studies on palmprint recognition systems have progressed towards the development of unconstrained, contactless and unrestricted background techniques. The most common problem encountered in these studies is the alignment arising from the free movement of the hand. Despite 3D hand-acquisition devices which offer extra recognition features to overcome this problem, the applicability of these devices is low because of their increased cost. In this study, a stereo camera was proposed. Although due to matching problems, it is difficult to achieve precise, distinct feature extraction in the unrestricted 3D environment used for palmprint recognition, the orientation of the hand in 3D space can be determined by obtaining depth information. In this study, the depth information was extracted by using the binocular stereo approach. First, the orientation of the hand was estimated by fitting a surface model associated with the eigenvectors of the depth information. Pose correction was then accomplished by establishing a relationship between the orientation and the images. The pose correction greatly relieved the perspective distortion that usually occurs within the various poses of the hands. Next, the region of interest was determined by performing segmentation on the corrected images using the Active Appearance Model (AAM). The palmprint features were then extracted via Gabor-based Kernel Fisher Discriminant Analysis. In order to demonstrate the performance of the proposed approach, a new dataset was compiled from stereo images within various scenarios collected from 138 different individuals. As a result of these experimental studies, the EER values, especially on the images captured from different hand orientations in 3D, were reduced from around 14–0.75%. With the help of this suggested approach, the palmprint recognition system was transformed into a more portable form by removing the closed-box mechanisms and equipment restricting movement of the hand. This system can automatically perform pose estimation, hand segmentation and recognition processes without any special intervention.
- MakaleYalnız künye2016X ve Gama-Işını Dedektörlerinde Ölü Zaman Düzeltmesi İçin Bir Program
Bingöl, Özkan; Kaya, Selim; Karabidak, Salih Mustafa; Kaya, Ali
X ya da gama ışını detektörlerinin kullanıldığı spektrometrik kantitatif ve kalitatif analizlerinde, analizlerin doğruluğu için sayma kayıplarının belirlenmesi ve telafi edilmesi önemlidir. Spektrometrelerdeki bu sayma kayıpları uzatılan ve uzatılmayan sistem ölü zamanlarından meydana gelir. Bu çalışmada, bu kayıpları telafi etmek için bir bilgisayar program geliştirilmiştir. Programı test etmek için deneysel çalışmalar yapılmıştır. Program, DELPHI program derleyicisiyle kodlanmıştır. Program, ölü zaman düzeltme, özgün pik arama, özgün grafik modülü, yazılım arayüzü ve program sonucunu rapor halinde veren rapor modüllerden oluşmaktadır. Bu programda düzeltme yapılmamış veya düzeltme yapılmış spektrumları ayrı ayrı irdeleme imkânı sağlanmıştır
- MakaleYalnız künye2016Orman Amenajman Planlarının Konumsal Simülasyon Modeli İle Hazırlanması
Kadıoğulları, Ali İhsan; Bingöl, Özkan; Keleş, Sedat; Baskent, Emin Zeki
Bu makalede, ekosistem tabanlı çok amaçlı planlama anlayışına göre geliştirilmiş bir simülasyon tabanlı konumsal orman amenajmanı planlama modeli tanıtılmıştır. Öncelikle modelin genel mimari çatısı kısaca açıklanmıştır. Model daha sonra örnek bir orman planlama biriminde uygulanmıştır. Bu amaçla konumsal özellikleri dikkate almayan bir temel simülasyon senaryosu ile farklı konumsal özelliklerin yer aldığı üç farklı planlama senaryosu olmak üzere toplamda dört tane planlama senaryosu üretilmiştir. Bu planlama senaryoları değişik performans göstergeleri (eta, dikili servet, net karbon birikimi, simülasyon süresi sonundaki yaş sınıfı dağılımı) kapsamında karşılaştırılmıştır. Planlama senaryolarının karşılaştırılması sonucunda, konumsal olmayan planlama senaryosu ile konumsal planlama senaryoları arasında eta açısından büyük farklılıkların olmadığı saptanmıştır. Ancak, konumsal planlama senaryoları ile birlikte orman ekosisteminde konumsal düzenlemenin etkin bir şekilde yapıldığı ve daha uygulanabilir orman amenajman planlarının üretilebileceği belirlenmiştir
- MakaleYalnız künye2015Konumsal Orman Amenajmanı Planlaması: Temel Kavramlar Ve Modelleme Teknikleri
Kadıoğulları, Ali İhsan; Bingöl, Özkan; Keleş, Sedat; Baskent, Emin Zeki
Ormanların ekosistem tabanlı çok amaçlı planlanması (ETÇAP) yaklaşımına uygun amenajmanplanların hazırlanması aşamasında, konumsal özelliklerin planlara yansıtılması ile farklı konumsalplanlama alternatiflerin geliştirilmesi büyük önem arz etmektedir. Konumsal orman amenajmanplanlaması, karar vericilere bütünleşik planlama yaklaşımı çerçevesinde, stratejik ve taktiksel düzeydeseçenekler sunmakta ve konumsal analizler yaparak en iyi kesim düzenini oluşturmasına olanakvermektedir. Bu makalede öncelikle konumsal planlamanın ormancılıktaki önemi ile Türkiye ormancılığıaçısından bir değerlendirmesi yapılmıştır. Daha sonra konumsal orman amenajmanı planlaması ile ilgilitemel kavramlar ayrıntılı olarak açıklanmıştır. Son olarak konumsal orman planlamasında yaygın olarakkullanılan önemli sezgisel karar verme teknikleri (modelleme teknikleri) ile dünya ormancılığındakonumsal planlama kapsamında geliştirilmiş ve kullanılmakta olan konumsal karar destek sistemleritanıtılmıştır. Sonuçta, konumsal orman planlamasının ETÇAPda yer alması gereken önemli bir planlamatekniği olduğu vurgulanmıştır.
- MakaleAçık Erişim2015Controlling spatial forest structure with spatial simulation in forest management planning: A case study from Turkey
Kadiogullari, Ali Ihsan; Baskent, Emin Zeki; Keles, Sedat; Bingöl, Özkan
Decision Support Systems (DSS) is widely used to develop spatially explicit forest management plans through the integration of spatial parameters. As a part of this study, a simulation-based spatial DSS, the ETÇAPSimülasyon program was developed and tested in a case study area. The system has the capability to control the spatial structure of forests based on a geodatabase. Geographical Information Systems (GIS) was used to generate the database, using spatial parameters including opening size, block size and green-up delay in addition to other attribute data such as the empirical yield table and the product assortment table. Based on the simulation technique, a spatial forest management model was developed to link strategic planning with tactical planning on a stand base and to present results with a number of performance indicators. One important component of the model determined all spatial characteristics with spatial parameters and patch descriptions. A stand growth and yield simulation model (BARSM) based on the relationship between current and optimal basal area development was also generated to project future stand characteristics and analyze the effects of various silvicultural treatments. A number of spatial forest management strategies were developed to generate spatially implementable harvest schedules and perform spatial analyses. The forest management concept was enhanced by employing a spatial simulation technique to help analyzing the ecosystem structure. Spatial characteristics for an on- The-ground forest management plan were then developed. The model was tested in Altinoluk Planning Unit (APU) using a spatial simulation technique based on various spatial parameters. The results indicated that the spatial model was able to satisfy the spatial restriction requirements of the forest management plan.