Edge Detection Using Integrate and Fire Neuron

Year 2019, Volume: 23 Issue: 2, 611 - 616, 25.08.2019

Mürsel Ozan İncetaş , Rukiye Uzun Arslan

https://doi.org/10.19113/sdufenbed.570597

Cited By: 1

Abstract

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

Topla ve Ateşle Nöron Modeli Kullanılarak Kenar Algılama

Abstract

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.

Keywords

Kenar algılama, Alıcı bölge, İğnecikli sinir ağları

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