Browsing by Author "Ozkan,A."
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Conference Object Citation Count: 19Kinshipgan: Synthesizing of Kinship Faces from Family Photos by Regularizing a Deep Face Network(IEEE Computer Society, 2018) Özkan, Akın; Ozkan,A.; Department of Electrical & Electronics EngineeringIn this paper, we propose a kinship generator network that can synthesize a possible child face by analyzing his/her parent's photo. For this purpose, we focus on to handle the scarcity of kinship datasets throughout the paper by proposing novel solutions in particular. To extract robust features, we integrate a pre-trained face model to the kinship face generator. Moreover, the generator network is regularized with an additional face dataset and adversarial loss to decrease the overfitting of the limited samples. Lastly, we adapt cycle-domain transformation to attain a more stable results. Experiments are conducted on Families in the Wild (FIW) dataset. The experimental results show that the contributions presented in the paper provide important performance improvements compared to the baseline architecture and our proposed method yields promising perceptual results. © 2018 IEEE.Conference Object Citation Count: 1Method proposal for distinction of microscope objectives on hemocytometer images;(Institute of Electrical and Electronics Engineers Inc., 2016) Özkan, Akın; Isgor,S.B.; İşgör, Sultan Belgin; Şengül, Gökhan; Department of Electrical & Electronics Engineering; Chemical Engineering; Computer EngineeringHemocytometer is a special glass plate apparatus used for cell counting that has straight lines (counting chamber) in certain size. Leveraging this special lam and microscope, a cell concentration on an available cell suspension can be estimated. The automation process of hemocytometer images will assist several research disciplines to improve consistency of results and to reduce human labor. Different objective measurements can be utilized to analyze a cell sample on microscope. These differences affect the detail of image content. Basically, while the objective value is getting increased, image scale and detail level taken from image will increase, yet visible area becomes narrower. Due to this variation, different self-cell counting approaches should be developed for images taken with different objective values. In this paper, using the hemocytometer images gathered from a microscope, a novel approach is introduced for which can estimate objective values of a microscope with machine learning methods automatically. For this purpose, a frequency-based visual feature is proposed which embraces hemocytometer structure well. As a result of the conducted tests, %100 distinction accuracy is achieved with the proposed method. © 2016 IEEE.