Akademik Veri Yönetim Sistemi
Tezin Türü: Yüksek Lisans
Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü, Türkiye
Tezin Onay Tarihi: 2013
Öğrenci: AHMET ERSÖZ
Danışman: İLKAY ULUSOY
Özet:Classifier combination and selection methods are becoming popular in vision research. Classifier fusion studies started to take the place of continuous development of new algorithms. In this study, template matching methods are used as classifiers and the results of the template matching methods from satellite images are taken as input to the fusion center. Template matching methods are adapted to different classifier fusion methods. In literature, there is not any performance measurement standard for the binary template matching output images. In order to analyze and compare the template matching methods and the classifier fusion methods, two performance measurement methods are proposed. In one of them, pixel-by-pixel intersection of the output image and the ground truth image are considered. In the other method, the output image is considered as a set of objects and the intersection of the object in the output image and the ground truth image is analyzed. The individual performance of the template matching methods is highly dependent on the threshold values used in the methods. When combining the template matching results, choosing optimal threshold is important to analyze the performance of the classifier fusion method. There have been very few studies for optimizing the fusion system performance and the studies have only been presented on decision level fusion. As a final task, a method for optimizing the performance in score (raw output) level fusion system is proposed. The results are quite promising such that the outputs of the proposed method outperformed to most of the score level fusion methods in the literature.