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Author: Sengul, GokhanLanguage: tr

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Now showing 1 - 4 of 4
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    Conference Object
    Citation - WoS: 1
    Gender Prediction by Using Local Binary Pattern and K Nearest Neighbor and Discriminant Analysis Classifications
    (Ieee, 2016) Camalan, Seda; Çamalan, Seda; Sengul, Gokhan; Şengül, Gökhan; Çamalan, Seda; Şengül, Gökhan; Information Systems Engineering; Computer Engineering; Information Systems Engineering; Computer Engineering
    In this study, gender prediction is investigated for the face images. To extract the features of the images, Local Binary Pattern (LBP) is used with its different parameters. To classify the images male or female, K-Nearest Neighbors (KNN) and Discriminant Analysis (DA) methods are used. Their performances according to the LBP parameters are compared. Also classification methods' parameters are changed and the comparison results are shown. These methods are applied on FERET database with 530 female and 731 male images. To have better performance, the face parts of the images are cropped then feature extraction and classification methods applied on the face part of the images.
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    Article
    The Effect of Statistically Constrained Minimum Mean Square Estimation Algorithm Which Is Used for Human Head Tissue Conductivity Estimation To Source Localization
    (Journal Neurological Sciences, 2012) Sengul, Gokhan; Şengül, Gökhan; Baysal, Ugur; Şengül, Gökhan; Computer Engineering; Computer Engineering; Computer Engineering
    Determining the electrical active regions of human brain by using EEG and/or MEG data is known as "EEG/MEG bioelectromagnetic inverse problem" or "source localization". A typical source localization system intakes not only EEG/MEG data but also geometry information of subject/patient, a priori information about the electrically active sources, the number and 3-D positions of measurement electrodes and conductivities/resistivities of the tissues in the head model. In this study we investigated the conductivity estimation performance previously proposed Statistically Constrainted Minimum Mean Square Error Estimation (MiMSEE) algorithm by simulation studies and we also investigated the effect of the estimation to source localization activities. In simulation studies we used a three-layered (composed of scalp, skull and brain regions) realistic head model to estimate 100 different conductivity distributions in vivo. As a result we found that the proposed algorithm estimates the conductivity of scalp with an average error of 23%, the conductivity of skull with an average error of 40% and finally the conductivity of brain with an average error of 17%. In the second part of the study we compared the source localization errors for two cases: one, when the average conductivities of tissues given in the literature are used, and second when the subject-specific conductivity estimation is performed with MiMSEE algorithm. The results showed 10.1 mm localization error is obtained when the average conductivities given in the literature are used and 2.7 mm localization is obtained when subject-specific conductivity estimation is performed with MiMSEE algorithm. The results shows that the localization error is reduced by 73.07% when subject-specific conductivity estimation is performed with MiMSEE algorithm. We conclude that using the conductivities obtained from MiMSEE algorithm reduces the source localization error and we recommend to perform subject-specific conductivity estimation for source localization applications.
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    Conference Object
    Citation - WoS: 1
    Method Proposal for Distinction of Microscope Objectives on Hemocytometer Images
    (Ieee, 2016) Ozkan, Akin; Isgor, S. Belgin; Sengul, Gokhan
    Hemocytometer 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.
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    Article
    Analysis of Turkey's Institutional Open Repositories: an Example of Dokuz Eylul University Institutional Open Repository
    (Turkish Librarians Assoc, 2012) Erturk, Korhan Levent; Sengul, Gokhan
    After the declaration of the Budapest Open Access Initiative in 2001, institutional open repositories are known as the most important tool of the self archiving, which is also known as green road. There are 26 institutional repositories, which are all compatible to international standards. All the institutional open repositories of Turkey mentioned before are listed in international open archive directories. In this study institutional open repository of Dokuz Eylul University is examined and institutional open repositories of Turkey are discussed.