Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Author: search.filters.author.Özuysal, MustafaWoS Q: search.filters.wosQuartile.N/A

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Now showing 1 - 6 of 6
  • Conference Object
    Citation - WoS: 1
    Citation - Scopus: 2
    Scene text localization using keypoints
    (Institute of Electrical and Electronics Engineers Inc., 2015) Erdoğmuş, Nesli; Özuysal, Mustafa
    Scene text localization and recognition (also known as text localization and recognition in real-world images, nature scene OCR or text-in-the-wild problem) is an open problem, attracting increasing interest from researchers. In this paper, we address the localization issue and leave the recognition part out of its scope. For the purpose of scene text localization, Scale-Invariant Feature Transform (SIFT) keypoints are extracted from the images and classified as text and non-text. Subsequently, the text keypoints are utilized to compute the bounding boxes around text regions. The proposed technique is tested on the database of ICDAR 2013 Robust Reading Competition-Challenge 2 and the experimental results are reported in detail. Although the idea introduced here is still at its infancy, it is observed to achieve remarkable results and due to the fact that there is a large room for improvement, it is found to be promising.
  • Conference Object
    A Detailed Analysis of Mser and Fast Repeatibility
    (Institute of Electrical and Electronics Engineers Inc., 2015) Uzyıldırım, Furkan Eren; Köksal, Ali; Özuysal, Mustafa
    This paper investigates the relationship between the MSER and FAST repeatability and changes in various camera parameters. By employing a realistic view synthesis methodology, it is possible to observe the effect of small parameter changes on the repeatability. Furthermore, for the analysis of MSER repeatability, a convex hull approach is proposed instead of fitting ellipses to the MSER region. This yields a better approximation to the MSER region without significantly increasing computation time.
  • Conference Object
    Deep Convolutional Neural Networks for Viability Analysis Directly From Cell Holograms Captured Using Lensless Holographic Microscopy
    (The Chemical and Biological Microsystems Society (CBMS), 2019) Delikoyun, Kerem; Çine, Ersin; Anıl İnevi, Müge; Özçivici, Engin; Özuysal, Mustafa; Tekin, Hüseyin Cumhur
    Cell viability analysis is one of the most widely used protocols in the fields of biomedical sciences. Traditional methods are prone to human error and require high-cost and bulky instrumentations. Lensless digital inline holographic microscopy (LDIHM) offers low-cost and high resolution imaging. However, recorded holograms should be digitally reconstructed to obtain real images, which requires intense computational work. We introduce a deep transfer learning-based cell viability classification method that directly processes the hologram without reconstruction. This new model is only trained once and viability of each cell can be predicted from its hologram. © 2019 CBMS-0001.
  • Conference Object
    Citation - WoS: 5
    Citation - Scopus: 8
    Lensless Digital In-Line Holographic Microscopy for Space Biotechnology Applications
    (Institute of Electrical and Electronics Engineers Inc., 2019) Delikoyun, Kerem; Çine, Ersin; Anıl İnevi, Müge; Özuysal, Mustafa; Özçivici, Engin; Tekin, Hüseyin Cumhur
    Biomechanical changes at cellular level can dramatically affect living organisms in both aviation and space applications. Weightlessness induces morphological alteration of cells, which leads to tissue loss. Therefore, scientists have been studying the effect of weightlessness using cell culture based biological experiments using conventional microscopes. However, strict requirements regarding cost, weight and functionality limit the use of conventional microscopes in space environment. Lensless digital in-line holographic microscopy enables to use low-weight, low-cost and robust elements, such as a light emitting diode (LED), an aperture and an imaging sensor, instead of bulky, expensive and fragile optical elements, such as lenses, mirrors and filters. This technology offers a high field of view compared to conventional microscopes without affecting the resolution and it is also suitable for remote sensing applications with automated imaging capabilities. Here, we present a portable digital in-line holographic microscopy platform that allows to visualize cells and to analyze their viability in a microfluidic chip. The platform offers microscopic imaging with 1.55 mu m spatial resolution, 21.7 mm(2) field of view and image coloring capability. This platform could potentially play an important role in space biotechnology applications by enabling low-cost, high-resolution and portable monitoring of cells.
  • Conference Object
    Mobil Nesne Takibinin Hızlandırılması
    (Institute of Electrical and Electronics Engineers Inc., 2016) Özuysal, Mustafa
    Bu bildiride modern işlemcilerin Tekil İşlem Çoklu Veri (TİÇV) komutlarıyla hızlandırılmış bir nesne takip modülü içeren mobil bir artırılmış gerçeklik uygulaması sunulmaktadır. Hem standart C++ hem de ARM işlemciler için geliştirilen TİÇV komut seti olan NEON ile kodlanmış verimli bir Sıfır Ortalamalı Farkların Kareleri Toplamı (SOFKT) yöntemi detaylandırılmıştır. Bu iki yöntemin mobil cihaz üzerinde çalışma hızları ölçülerek karşılaştırılmıştır.
  • Book Part
    Citation - WoS: 299
    Citation - Scopus: 406
    Introduction To Machine Learning
    (Humana Press, 2014) Baştanlar, Yalın; Özuysal, Mustafa
    The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods.