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    Detection of Optic Disc Localization from Retinal Fundus Image Using Optimized Color Space
    (Springer, 2022) Toptas, Buket; Toptas, Murat; Hanbay, Davut
    Optic disc localization offers an important clue in detecting other retinal components such as the macula, fovea, and retinal vessels. With the correct detection of this area, sudden vision loss caused by diseases such as age-related macular degeneration and diabetic retinopathy can be prevented. Therefore, there is an increase in computer-aided diagnosis systems in this field. In this paper, an automated method for detecting optic disc localization is proposed. In the proposed method, the fundus images are moved from RGB color space to a new color space by using an artificial bee colony algorithm. In the new color space, the localization of the optical disc is clearer than in the RGB color space. In this method, a matrix called the feature matrix is created. This matrix is obtained from the color pixel values of the image patches containing the optical disc and the image patches not containing the optical disc. Then, the conversion matrix is created. The initial values of this matrix are randomly determined. These two matrices are processed in the artificial bee colony algorithm. Ultimately, the conversion matrix becomes optimal and is applied over the original fundus images. Thus, the images are moved to the new color space. Thresholding is applied to these images, and the optic disc localization is obtained. The success rate of the proposed method has been tested on three general datasets. The accuracy success rate for the DRIVE, DRIONS, and MESSIDOR datasets, respectively, is 100%, 96.37%, and 94.42% for the proposed method.
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    Smoke Detection Using Texture and Color Analysis in Videos
    (Ieee, 2017) Toptas, Murat; Hanbay, Davut
    The delays in the detection of fire in fire detection systems continue to be a life threatening problem for living things. Techniques based on image processing have been developed in order to remove this problem and minimize the detection period. This study also focused on the smoke image that appeared before the flame at the time of the fire. Smoke detection can provide earlier notification than flame detection. In the first step of the proposed method, smoke zone was detected with YUV color space. After than the Gray Level Co-Occurrence Matrix (GLCM) was used to extract the features that represent the smoke images. At last, these features are used to classify the smoke and non-smoke space by using Support Vector Machines (SVM).