Threshold selection based on type-2 fuzzy 2-partition entropy approach

Ouarda, Assas; Benmedour, Fadila

doi:10.1109/icocs.2014.7060943

Cited by 1 publication

(1 citation statement)

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“…Besides, the intuitionistic type-2 fuzzy set has been formed as an extension of intuitionistic fuzzy set for handling uncertainty. Ouarda [11] proposed a new thresholding method for image segmentation using type-2 fuzzy 2-partition entropy, and type-2 fuzzy sets represent fuzzy sets with fuzzy membership values. Shi [12] used the interval type-2 fuzzy approach into the framework of active contour model.…”

Section: Introductionmentioning

confidence: 99%

A New Type-2 Fuzzy Algorithm for Unmanned Aerial Vehicle Image Segmentation

Zhong¹,

Liu²,

Luo³

et al. 2017

IJMUE

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This paper proposes a new type-2 fuzzy algorithm for the Unmanned Aerial Vehicle (UAV) image segmentation. In this algorithm, a logarithmic function is applied to transforming the membership of each pixel which is derived from the Fuzzy C-Means (FCM) algorithm; In other words, this logarithmic function defines a new membership function which can update the cluster centers effectively. The proposed method is called the Logarithmic Functional Fuzzy C-Means Algorithm (LF-FCM) used for the UAV image segmentation. The proposed method achieves better segmentation results than those of the K-means, FCM clustering algorithms and possibilistic c-means algorithm (PCM) in our comparative study for UAV images. A numerical example is provided to exam the behavior and effectiveness of all the algorithms tested. Experimental results demonstrate that the proposed LF-FCM algorithm is efficient and robust for natural UAV images. The Davies-Bouldin (DB) index, the rate of misclassification (Perror) and the

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Section: Introductionmentioning

confidence: 99%