Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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

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Department: search.filters.department.İzmir Institute of Technology. Electrical and Electronics EngineeringSubject: search.filters.subject.Image processing

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  • Article
    Citation - WoS: 3
    Citation - Scopus: 3
    Elimination of Useless Images From Raw Camera-Trap Data
    (Türkiye Klinikleri Journal of Medical Sciences, 2019) Tekeli, Ulaş; Baştanlar, Yalın
    Camera-traps are motion triggered cameras that are used to observe animals in nature. The number of images collected from camera-traps has increased significantly with the widening use of camera-traps thanks to advances in digital technology. A great workload is required for wild-life researchers to group and label these images. We propose a system to decrease the amount of time spent by the researchers by eliminating useless images from raw camera-trap data. These images are too bright, too dark, blurred, or they contain no animals To eliminate bright, dark, and blurred images we employ techniques based on image histograms and fast Fourier transform. To eliminate the images without animals, we propose a system combining convolutional neural networks and background subtraction. We experimentally show that the proposed approach keeps 99% of photos with animals while eliminating more than 50% of photos without animals. We also present a software prototype that employs developed algorithms to eliminate useless images.
  • Article
    Citation - WoS: 3
    Citation - Scopus: 3
    Texture Analysis of Polymer Modified Bitumen Images
    (Carl Hanser Verlag GmbH & Co. KG, 2011) Gümüştekin, Şevket; Topal, Ali; Şengöz, Burak
    This study aims to analyze the textural features extracted from microscopic images of elastomeric and plastomeric type polymer modified bitumen (PMB) including five different types and contents of polymers. Fluorescence microscopy was used to capture microscopic images from thin films of PMB samples at different magnification scales (400×, 100×, and 40×). Gabor filters were utilized to extract the textural features of bitumen images. The features were used in three different query tests to quantify their representation capacity. The K nearest neighbor classifier was tested using leave-one-out cross validation. Textural analysis on the captured images provided numerical results that are in compliance with subjective visual tests. © 2011 Carl Hanser Verlag, Munich, Germany.