COMPARISON OF CLASSICAL AND FUZZY EDGE DETECTION METHODS

Yıl 2024, Cilt: 12 Sayı: 1, 177 - 191, 01.03.2024

Gulcihan Ozdemir

https://doi.org/10.36306/konjes.1116833

Öz

Edge detection is one of the challenging problems in image processing. Four different classical edge detection methods—Sobel, Prewitt, Roberts, and Canny—and type-1 and type-2 fuzzy logic-based edge detection methods are applied to analyze two separate datasets with various properties. The datasets are STARE which contains medical images of the retina and BIPED which contains images of the street. Furthermore, two separate hybrid fuzzy logic methods are implemented. The type-1 and type-2 fuzzy inference techniques are combined to produce the hybrid-1 and hybrid-2 approaches, using the "AND" and "OR" logic operators. We compare the simulation results for each technique using three different image quality metrics. These are Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR), and Structural Similarity Index (SSIM). The type-2 fuzzy technique outperformed the hybrid-1 fuzzy method in visual quality metrics comparison, demonstrating superior blood vessel recognition on the STARE retinal image dataset—a dataset that more closely resembles the human visual system. Using the BIPED street image dataset, the hybrid-1 fuzzy approach outperformed the Roberts method. The hybrid-1 fuzzy technique showed good results in the second order for both kinds of datasets. Any data and general applications can take advantage of it.

Anahtar Kelimeler

Edge detection, Fuzzy inference system, Hybrid fuzzy inference system, Image processing, Type-1 fuzzy inference system, Type-2 fuzzy inference system

Klasik ve Bulanık Kenar Algılama Yöntemlerinin Karşılaştırılması

Öz

Görüntü işlemedeki zorlu problemlerden biri kenar algılamadır. Bu çalışmada, literatürden Sobel, Prewitt, Roberts ve Canny olmak üzere dört farklı klasik kenar algılama yöntemi ile tip-1 bulanık çıkarım ve tip-2 bulanık çıkarım olan iki bulanık mantık tabanlı kenar algılama yöntemi iki farklı veri kümesine uygulanmıştır. Veri setlerinden biri retina görüntüleri olan STARE, diğeri ise sokak görüntülerini içeren BIPED veri setidir. Her yöntemin simülasyon sonuçları üç farklı hata metrikleri kullanılarak karşılaştırılmıştır. Bunlar Tepe Sinyal Gürültü Oranı (PSNR), Yapısal Benzerlik Endeksi (SSIM) ve Ortalama Karesel Hata’dır (MSE). Şu sonuca vardık; tip-2 bulanık yöntem kan damarı tespiti için tıbbi görüntüler için en iyisidir ve sokak görüntülerinde de kullanılabilir, daha genel görüntüler için tip-1 bulanık yöntem seçilebilir ve PSNR metriği açısından daha genel görüntüler için Roberts en iyisidir ancak bizim fikrimize göre tip-1 bulanık yöntemin sonucu görsel olarak daha tatmin edicidir.

Anahtar Kelimeler

Bulanık çıkarım sistemi, Tip-1 bulanık çıkarım sistemi, Tip-2 bulanık çıkarım sistemi, Kenar algılama, Görüntü işleme

Kaynakça

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