Eliptik Fourier Serilerinde Yaprak Sınıflandırma İçin Parametre Seçimi

Year 2022, Volume: 24 Issue: 71, 375 - 381, 16.05.2022

Hüseyin Atasoy Yakup Kutlu

https://doi.org/10.21205/deufmd.2022247104

Abstract

Eliptik Fourier serileri metodu kontur analizinde göreceli olarak eski ancak güçlü bir yöntemdir. Bu metodu daha önce “BioMorp” isimli bir bilgisayar programı için bir modül olarak hazırlamıştık. Bu modülün iki önemli parametresi mevcuttur; birincisi konturu temsil edecek fonksiyonu meydana getiren harmoniklerin sayısıdır (sinüs-kosinüs terimleri). İkinci parametre, sinüs ve kosinüs terimlerinin sürekli toplamı olan fonksiyonun örneklenmesi ile elde edilen örneklerin sayısıdır. Bu çalışmada, bu iki parametrenin yaprak sınıflandırma üzerindeki etkileri incelenmiştir. Ayrıca koordinatların yeniden yapılandırılmasıyla elde edilen sonuçlar ile Fourier katsayılarının genlikleri kullanılarak elde edilen sonuçlar karşılaştırılmıştır. Koordinatların yeniden yapılandırılması ile minimum 15 sinüs-kosinüs terimi ve kontur başına 20 örnek kullanıldığında elde edilen en yüksek başarım değeri 75,197% iken, Fourier katsayılarının genlikleri kullanıldığında minimum 29 terim ile 69,953% olarak ölçülmüştür.

Keywords

fourier serileri, kontur analizi, kontur sınıflandırma

Parameter Selection in Elliptical Fourier Series for Leaf Classification

Abstract

Elliptical Fourier series method is a relatively old but powerful method in contour analysis. We have previously implemented this method as a module for a computer program called “BioMorph”. There are two significant parameters in this module; the first one is the number of harmonics (sine-cosine terms) that are calculated to form a function which represents the contour. The second parameter is the number of samples that are collected by sampling the function which is a continuous function of sums of sine and cosine terms. In this study, the effects of those two parameters to leaf classification are investigated. Also classification results obtained using reconstructed coordinates and using magnitudes of Fourier coefficients are compared. While the highest classification accuracy is obtained as 75,197% with minimum 15 sine-cosine terms and 20 samples per contour using reconstructed coordinates, it is obtained as 69,953% using magnitudes of Fourier coefficients with minimum 29 terms.

Keywords

fourier series, contour analysis, contour classification

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