Browsing by Subject "State-of-art methods"

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    Applying deep learning in augmented reality tracking
    (IEEE, 2016-11-12) Akgül, Ömer; Penekli, H. I.; Genç, Y.
    An existing deep learning architecture has been adapted to solve the detection problem in camera-based tracking for augmented reality (AR). A known target, in this case a planar object, is rendered under various viewing conditions including varying orientation, scale, illumination and sensor noise. The resulting corpus is used to train a convolutional neural network to match given patches in an incoming image. The results show comparable or better performance compared to state of art methods. Timing performance of the detector needs improvement but when considered in conjunction with the robust pose estimation process promising results are shown. © 2016 IEEE.
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    Scene classification with random forests and object and color distributions
    (IEEE, 2013) İşcen, Ahmet; Gölge, Eren; Armağan, Anıl; Duygulu, Pınar
    We propose a method to recognize the scene of an image by finding the objects and the colors it contains. We approach this problem by creating a binary vector of detected objects and a histogram of the colors that the image contains. We then use these features to train a random forest classifier in order to determine the scene of each image. For class-based classifiers, our method gives comparable results with the state of art methods, such as Object Bank method, for the indoor scene dataset that we used. Additionally, while well-known methods are computationally expensive, our method has a low computational cost. © 2013 IEEE.