Adaptif 2-D LMS Filtre Gömülü Kenar Algılama Uygulaması

Abstract

Bu çalışmada, iki-boyutlu en küçük ortalama kareler (TDLMS) adaptif filtresinin etkisi çeşitli kenar algılama sistemlerinin içerisine gömülerek tartışılmıştır. TDLMS ve kenar algılama modülleri sistem şeması içerisinde seri sırada çalışacak şekilde yerleştirilmiştir. TDLMS algoritması birçok değişik görüntü işleme uygulamalarında yaygın olarak kullanılmaktadır. Özellikle filtre katsayılarının herhangi bir öncül varsayıma ihtiyaç duymadan güncellenebiliyor olması, TDLMS filtresine iki boyutlu sinyal işleme uygulamalarında çok üstün avantajlar sağlamaktadır. Bu çalışmada, literatürde sıkça kullanılan Canny, Sobel, Prewitt, Roberts ve LoG kenar algılama algoritmaları üzerindeki TDLMS’in sağladığı performans artışı incelenmiştir. Gerçekleştrilen benzetimlerde, TDLMS’in yüksek SNR değerine sahip görüntülerden ziyade, düşük SNR değerine sahip görüntülerde kenar algılama algoritmalarının performasında artışa sebep olduğu gözlemlenmiştir. Özellikle, düşük SNR durumunda Sobel, Prewitt ve Roberts algoritmalarındaki performans artışı, Canny ve LoG algoritmalarındaki performans artışına göre daha fazla olmuştur. Etkisi fazla olmamakla birlikte yüksek SNR durumunda ise Canny algoritmasındaki performans artışı Sobel, Prewitt, Roberts ve LoG algoritmalarına kıyasla daha fazla olduğu görülmüştür.

Keywords

• 2-D Adaptif Fitre
• TDLMS
• Kenar Algılama Algoritması
• Canny
• Sobel
• Prewitt
• Roberts
• LoG

Adaptive 2-D LMS Filter Embedded Edge Detection Application

Abstract

In this numerical study the effect of embedding two-dimensional least mean square (TDLMS) adaptive filter into various edge detection systems is discussed. TDLMS and edge detection modules are arranged in the system scheme in a manner such that they work sequentially. TDLMS algorithm is commonly used in many various image processing applications. Due to its ability of updating filter coefficients without needing any a priori assumptions, TDLMS provides superior advantegeous in 2-D signal processing applications. We investigated the performance increment of TDLMS especially on the commonly used edge detection algortihms in the literature such as Canny, Sobel, Prewitt, Roberts and LoG (Laplacian of Gaussian). It is observed that embedding TDLMS is particularly useful in edge detection for low SNR images comparing to high SNR images. The simulation results clearly show TDLMS filter provides significant improvement for the edge detection implementation on a relatively lower SNR case comparing to a higher SNR case. Especially, TDLMS embedded Sobel, Prewitt and Roberts implementations have relatively better results than TDLMS embedded Canny and LoG implementations for a low SNR image. On the other hand, for relatively higher SNR case, embedding TDLMS filter into the edge detection system does not provide as much significant improvement as in relatively lower SNR case. But still, for a high SNR case, TDLMS embedded Canny implementation have relatively better results than TDLMS embedded Sobel, Prewitt, Roberts and LoG implementations.

Keywords

• 2-D Adaptive Filter
• TDLMS
• Edge Detection Algorithm
• Canny
• Sobel
• Prewitt
• Roberts
• LoG

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