Yazar "Arslan, Rukiye Uzun" seçeneğine göre listele

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    Edge detection using integrate and fire neuron model
    (2019) İncetaş, Mürsel Ozan; Arslan, Rukiye Uzun
    Edge detection is one of the most basic stages of image processing and have been used in many areas. Its purpose is to determine the pixels formed the objects. Many researchers have aimed to determine objects' edges correctly, like as they are determined by the human eye. In this study, a new edge detection technique based on spiking neural network is proposed. The proposed model has a different receptor structure than the ones found in literature and also does not use gray level values of the pixels in the receptive field directly. Instead, it takes the gray level differences between the pixel in the center of the receptive field and others as input. The model is tested by using BSDS train dataset. Besides, the obtained results are compared with the results calculated by Canny edge detection method.
  • [ X ]
    Öğe
    Effects of character recognition with shell histogram method using plate characters
    (2019) Arslan, Rukiye Uzun; Dikici, Sedat; İncetaş, Mürsel Ozan
    Character recognition is a study that has been used in various fields for many years. In character recognition, the aim is to identify the various texts, letters and symbols in the images as accurately and quickly as possible. In addition to the Optical Character Recognition (OCT) method, which is used as a very common method, there are many feature extraction methods in which character image features are compared. In this study, which is presented as another feature extraction method, the letters on the license plates are recognized. The characters were examined using the circular shape histogram technique and histograms were obtained from the sectors within the circular regions. Feature vectors for letter characters were created using character pixel densities in sectors. Feature vectors are analyzed linearly and an alternative quick character recognition method is presented. With the proposed method, the element numbers of the feature vectors are kept constant. In this way, both the processing speed is increased and the processing speed variations are minimized. The results show that the proposed method requires lesser parameters than the OCT method, but also has a significant success rate according to known feature extraction methods.