Topla ve Ateşle Nöron Modeli Kullanılarak Kenar Algılama

Kenar algılama, görüntü işlemenin en temel aşamalarından biridir ve birçok farklı alanda kullanılmaktadır. Kenar belirleme yöntemlerinin amacı görüntüyü oluşturan pikselleri belirlemektir. Çoğu araştırmacı, insan gözünün belirlediği gibi nesnelerin kenarlarını doğru algılamayı hedeflemiştir. Bu çalışmada, iğnecikli sinir ağ yapısına dayalı yeni bir kenar algılama tekniği önerilmiştir. Önerilen model, literatürde bulunanlardan farklı bir alıcı yapısına sahiptir ve doğrudan alıcı alandaki piksellerin gri seviye değerlerini kullanmamaktadır. Bunun yerine, girdi olarak alıcı alanın ortasındaki piksel ile diğerleri arasındaki gri seviye farklarını kullanarak kenar algılama işlemini gerçekleştirmektedir. Geliştirilen model, BSDS öğrenme veri seti kullanılarak test edilmiştir. Ayrıca, elde edilen sonuçlar Canny kenar algılama yöntemi yardımıyla hesaplananlar ile karşılaştırılmıştır.

Anahtar Kelimeler:

Kenar algılama, Alıcı bölge, İğnecikli sinir ağları

Edge Detection Using Integrate and Fire Neuron

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.

Keywords:

Edge detection, Receptive field, Spiking neuronal network,

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