Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 1Citation - Scopus: 1An Iris Segmentation Scheme Based on Bendlets(Springer London Ltd, 2023) Aghazadeh, Nasser; Abbasi, Mandana; Noras, ParisaDue to the effect of agents such as ambiance, transition channel, and other agents, images are polluted by noise during collection, transition, and compaction, leading to decrease image quality. Noise can decrease the accuracy of the next stages of image processing systems. Therefore, one of the vital stages in the novel processing systems is denoising. This article offers a novel image denoising approach using bendlets. Other multi-scale transformations (such as wavelets, curvelets, and shearlets) cannot recognize properties such as location, direction, and curvature of discontinuities well in piecewise stable images. To solve this problem, bendlets are suggested in this article. Bendlets differ from other multi-scale transformations in that an additional bending parameter is utilized for recognizing the curvature of discontinuities. Bendlets need a fewer number of coefficients to identify curvatures than other multi-scale transformations. Furthermore, they help to make the edges more obvious. The suggested approach is utilized on the UBIRIS.V2 database. It earns better accuracy and stability than other multi-scale transformations.Article Citation - WoS: 9Citation - Scopus: 12Intensity and Phase Stacked Analysis of a 40-Otdr System Using Deep Transfer Learning and Recurrent Neural Networks(Optica Publishing Group, 2023) Kayan, Ceyhun Efe; Yüksel Aldoğan, Kıvılcım; Gümüş, AbdurrahmanDistributed acoustic sensors (DAS) are effective apparatuses that are widely used in many application areas for recording signals of various events with very high spatial resolution along optical fibers. To properly detect and recognize the recorded events, advanced signal processing algorithms with high computational demands are crucial. Convolutional neural networks (CNNs) are highly capable tools to extract spatial information and are suitable for event recognition applications in DAS. Long short-term memory (LSTM) is an effective instrument to process sequential data. In this study, a two-stage feature extraction methodology that combines the capabilities of these neural network architectures with transfer learning is proposed to classify vibrations applied to an optical fiber by a piezoelectric transducer. First, the differential amplitude and phase information is extracted from the phasesensitive optical time domain reflectometer (40-OTDR) recordings and stored in a spatiotemporal data matrix. Then, a state-of-the-art pre-trained CNN without dense layers is used as a feature extractor in the first stage. In the second stage, LSTMs are used to further analyze the features extracted by the CNN. Finally, a dense layer is used to classify the extracted features. To observe the effect of different CNN architectures, the proposed model is tested with five state-of-the-art pre-trained models (VGG-16, ResNet-50, DenseNet-121, MobileNet, and Inception-v3). The results show that using the VGG-16 architecture in the proposed framework manages to obtain a 100% classification accuracy in 50 trainings and got the best results on the 40-OTDR dataset. The results of this study indicate that pre-trained CNNs combined with LSTM are very suitable to analyze differential amplitude and phase information represented in a spatiotemporal data matrix, which is promising for event recognition operations in DAS applications. (c) 2023 Optica Publishing GroupConference Object Citation - WoS: 2Citation - Scopus: 2Türkçe Manzara Metni Veri Kümesi(IEEE, 2017) Erdogmus, NesliScene text localization and recognition keeps attracting an increasing interest from researchers due to its valuable advantage in extracting content from real world images and in image retrieval via text search. Nevertheless, due to the fact that the majority of the image datasets that are commonly used in this field is comprised of text in English, the related studies have mostly been limited to a single language. On that account, in order to apply the technologies developed for scene text detection and recognition to Turkish scene text, analyze their performances and to develop Turkish language specific algorithms, a Turkish scene text database is collected for the first time in the literature. In this paper, the contents of this database, shortly called STRIT (Scene Text Recognition In Turkish), are detailed. Additionally, two baseline methods are tested to detect and recognize scene text in Turkish and the preliminary results are presented.