Yazar "Turhan, Mehmet Murat" seçeneğine göre listele

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    Determining Noise Performance of Co-Occurrence GMuLBP on Object Detection Task
    (Spie-Int Soc Optical Engineering, 2013) Alpaslan, Nuh; Turhan, Mehmet Murat; Hanbay, Davut
    Object detection is currently one of the most actively researched areas of computer vision, image processing and analysis. Image co-occurrence has shown significant performance on object detection task because it considers the characteristic of objects and spatial relationship between them simultaneously. CoHOG has achieved great success on different object detection tasks, especially human detection. Whereas, CoHOG is sensitive to noise and it does not consider gradient magnitude which significantly effects the object detection accuracy. To overcome these disadvantages the CoGMuLBP was proposed. CoGMuLBP uses a new statistical orientation assignment method based on uniform LBP instead of using the common gradient orientation In this study, detection accuracies of CoGMuLBP and CoHOG are calculated on three different datasets with NN classifier. In addition, to evaluate the noise performance of the methods, gaussian noises were added to test images and performances were recalculated. Numerical experiments performed on three different datasets show that 1) CoGMuLBP has higher detection accuracy than CoHOG; 2) using uniform LBP based gradient orientation improves detection accuracy; and 3) CoGMuLBP is more robust to gaussian noise and illumination changes. These results provide the effectiveness of CoGMuLBP for object detection.
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    Mean Shift Based Object Tracking Supported with Adaptive Kalman Filter
    (Ieee, 2015) Turhan, Mehmet Murat; Hanbay, Davut
    In this paper, mean shift algorithm and adaptive Kalman filter have been both utilized to realize object tracking in video sequences. Mean shift algorithm cannot give good results when the position of the tracked object is changed rapidly between sequential frames or the tracked object is occluded. In this paper, the first position of the tracked object is predicted by Kalman filter then mean shift algorithm starts to seek the object in this position. Bhattacharyya coefficient which is obtained from mean shift algorithm, is used to instantly update Kalman filters error covariance matrix and determine whether object is occluded or not. Experimental results demonstrate that the proposed method has been more efficient technique as compared to standard mean shift algorithm in case of occlusion and fast object tracking.
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    Uyarlamalı kalman filtresi destekli ortalama kayma tabanlı nesne takibi
    (İnönü Üniversitesi, 2016) Turhan, Mehmet Murat
    Bu tezde, ortalama kayma algoritması ve uyarlamalı Kalman filtresi birlikte kullanılarak görüntü dizilerinde nesne takibi gerçekleştirilmiştir. Ortalama kayma algoritması takip edilen nesnenin ardışık iki görüntü arasında hızlı yer değiştirmesi veya nesnenin başka nesneler tarafından engellenmesi gibi durumlarda iyi sonuçlar verememektedir. Yapılan çalışmada, takip edilen nesnenin aranacağı başlangıç konumu Kalman filtresi tarafından tahmin edilir ve ortalama kayma algoritması nesneyi bu konumda aramaya başlar. Ortalama kayma algoritmasından elde edilen Bhattacharyya katsayısı, Kalman filtresinin ölçüm hata kovaryans matrisini anlık güncellemede ve nesnenin engele maruz kalıp kalmadığına karar vermede kullanılır. Deneysel sonuçlar önerilen yöntemin nesnenin engellenme ve hızlı hareket etme durumları için standart ortalama kayma algoritmasına kıyasla daha etkili olduğunu göstermektedir.