Improved Interval Type-2 Fuzzy Subtractive Clustering for obstacle detection of robot vision from stream of Depth Camera

Nguyen, Mau Uyen; Ngo, Long Thanh; Dao, Thanh Tinh

doi:10.1109/isda.2012.6416658

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…These algorithms have been used for different purposes, such as modeling [14][15][16][17], creation of membership functions [18,19], image processing [20,21], and classification [22]. In recent years research has also been performed in the extension of other clustering algorithms using Type-2 Fuzzy Logic Techniques, such as the ones proposed in [13,[23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

An Extension of the Fuzzy Possibilistic Clustering Algorithm Using Type-2 Fuzzy Logic Techniques

Rubio

Castillo

Valdez

et al. 2017

Advances in Fuzzy Systems

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In this work an extension of the Fuzzy Possibilistic C-Means (FPCM) algorithm using Type-2 Fuzzy Logic Techniques is presented, and this is done in order to improve the efficiency of FPCM algorithm. With the purpose of observing the performance of the proposal against the Interval Type-2 Fuzzy C-Means algorithm, several experiments were made using both algorithms with well-known datasets, such as Wine, WDBC, Iris Flower, Ionosphere, Abalone, and Cover type. In addition some experiments were performed using another set of test images to observe the behavior of both of the above-mentioned algorithms in image preprocessing. Some comparisons are performed between the proposed algorithm and the Interval Type-2 Fuzzy C-Means (IT2FCM) algorithm to observe if the proposed approach has better performance than this algorithm.

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