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Journals / Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi / 2019 / Cilt: 23 - Sayı: 2

Edge Detection Using Integrate and Fire Neuron Model

Mürsel Ozan İNCETAŞ
Rukiye UZUN ARSLAN
Pages
611–616
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Besides, the obtained results are compared with the results calculated by Canny edge detection method.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Edge detection is one of the most basic stages of image processing and have been used in many areas.
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Key Findings & Data:Its purpose is to determine the pixels formed the objects.
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Conclusion & Impact:Besides, the obtained results are compared with the results calculated by Canny edge detection method.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Özet

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.

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Cite This Article

İNCETAŞ, M. O., ARSLAN, R. U. (2019). Edge Detection Using Integrate and Fire Neuron Model. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt: 23 - Sayı: 2, 611–616. https://ajindex.com/articles/310012

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