Gri Kurt Optimizasyon Algoritmasına Dayanan Çok Seviyeli İmge Eşik Seçimi

Year 2018, Volume: 21 Issue: 4, 841 - 847, 01.12.2018

Ismail Koc , Omer Kaan Baykan Ismail Babaoglu

https://doi.org/10.2339/politeknik.389613

Cited By: 3

Abstract

Çoklu eşikleme, görüntü işleme ve
örüntü tanıma için önemli bir görüntü işleme tekniğidir. Optimal bir eşik
değerinin seçimi görüntü eşiklemede en ciddi aşamalardan birisidir. İki seviye
segmentasyon eşik değeri yardımıyla orijinal resmi iki alt bölüme ayırmayı
içerirken, çoklu segmentasyon çok eşik değerlerini içermektedir. Özellikle çok
seviyeli görüntü eşiklemede, detaylı araştırmaya ilişkin hesaplama zamanı
tercih edilen eşik sayısıyla birlikte üstel olarak artmaktadır. Zor problemler
için, sürü zekâsı başarılı ve etkili optimizasyon metotlarından biri olarak
bilinmektedir.  Bu çalışmada, doğadaki
gri kurtların sosyal liderlik ve avcılık davranışlarını taklit eden son
zamanlarda önerilmiş sürü tabanlı meta sezgisel olan gri kurt algoritması (GWO)
çok seviyeli görüntü eşikleme probleminin çözümü için kullanılmaktadır.
Standart test resimleri üzerinde yapılan deneysel sonuçlar GWO algoritmasının
diğer modern algoritmalarla karşılaştırılabilir olduğunu göstermektedir.

Keywords

Çok seviyeli imge eşikleme, otsu metodu, sürü zekası, optimizasyon algoritmaları, gri kurt algoritması

Multilevel Image Thresholding Selection Based on Grey Wolf Optimizer

Abstract

ABSTRACT

Multilevel thresholding is an important image process technique for
image processing and pattern recognition. Selecting an optimal threshold value
is one of the most crucial phase in image thresholding. While bi-level
segmentation contains separating the original image into subdivided sections
with help of a threshold value, multilevel segmentation involves multi
threshold values. Especially in multilevel image tresholding, the computational
time of detailed search increases exponentially with the number of preferred
thresholds. For compelling problems, swarm intelligence is known as one of the
successful and influential optimization methods. In this paper, the grey wolf
optimizer (GWO), a recently proposed swarm-based meta-heuristic which imitates
the social leadership and hunting behavior of gray wolves in nature is employed
for solving the multilevel image thresholding problem. The experimental results
on standard benchmark images indicate that the grey wolf optimizer algorithm is
comparable with other state of the art algorithms.

Keywords

Multilevel image thresholding, otsu method, herd intelligence, optimization algorithms, gray wolf algorithm

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