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

Ouarda, Assas; Benmedour, Fadila

doi:10.1109/icocs.2014.7060943

Cited by 2 publications

(6 citation statements)

References 12 publications

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“…Here I and Î are the original and thresholded images of size MxN, respectively (Assas, 2014). The original image is segmented in two parts (background and object).…”

Section: Resultsmentioning

confidence: 99%

“…Of course, other linguistic hedges such as de-accentuation and accentuation can also be employed: (Assas, 2014) (Mendel, 2001)…”

Section: Type-2 Fuzzy Setsmentioning

confidence: 99%

“…Fuzzy threshoding is able to remove grayness ambiguities in a robust way. The type-1 fuzzy sets remove the vagueness/imprecision in the data but not the uncertainty in assigning membership values to the data) (Assas, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we are extending the use of fuzzy logic to a higher order, called type-2 fuzzy logic. We propose the fuzzy 2-partition using type-2 fuzzy logic with new membership functions (Assas, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Improvement of 2-Partition Entropy Approach Using Type-2 Fuzzy Sets for Image Thresholding

Ouarda

2015

International Journal of Applied Evolutionary Computation

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Thresholding is a fundamental task and a challenge for many image analysis and pre-processing process. However, the automatic selection of an optimum threshold has remained a challenge in image segmentation. The fuzzy 2-partition entropy approach for threshold selection is one of the best image thresholding techniques. In this work, an improvement of the later method using type-2 fuzzy sets is proposed to represent the imprecision or lack of knowledge of the expert in the choice of the membership function associated with the image. Two databases are used to evaluate its effectiveness: dataset of standard grayscale test images and MR Brain images. Experiment results show that the type-2 Fuzzy 2-partition entropy algorithm performs equally well in terms of the quality of image segmentation and leads to a good visual result.

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“…Here I and Î are the original and thresholded images of size MxN, respectively (Assas, 2014). The original image is segmented in two parts (background and object).…”

Section: Resultsmentioning

confidence: 99%

“…Of course, other linguistic hedges such as de-accentuation and accentuation can also be employed: (Assas, 2014) (Mendel, 2001)…”

Section: Type-2 Fuzzy Setsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improvement of 2-Partition Entropy Approach Using Type-2 Fuzzy Sets for Image Thresholding

Ouarda

2015

International Journal of Applied Evolutionary Computation

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

Cited by 2 publications

References 12 publications

Improvement of 2-Partition Entropy Approach Using Type-2 Fuzzy Sets for Image Thresholding

Improvement of 2-Partition Entropy Approach Using Type-2 Fuzzy Sets for Image Thresholding

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

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