Fuzzy sets for image processing and understanding

Bloch, Isabelle

doi:10.1016/j.fss.2015.06.017

Cited by 73 publications

(23 citation statements)

References 134 publications

(148 reference statements)

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“…In addition, the bacterial algorithm is applied to control the membership functions. The definition of fuzzy image processing is simply explained as an approach which is capable of processing integral images with their segments values as fuzzy sets [27]. Even though this process has three main steps like the normal fuzzy, it is based on image analysis rather than on input values.…”

Section: Intelligent Image Processing Algorithm (Iipa)mentioning

confidence: 99%

A Novel Edge Detection Algorithm for Mobile Robot Path Planning

Al-Jarrah

Al‐Jarrah

Roth

2018

Journal of Robotics

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A novel detection algorithm for vision systems has been proposed based on combined fuzzy image processing and bacterial algorithm. This combination aims to increase the detection efficiency and reduce the computational time. In addition, the proposed algorithm has been tested through real-time robot navigation system, where it has been applied to detect the robot and obstacles in unstructured environment and generate 2D maps. These maps contain the starting and destination points in addition to current positions of the robot and obstacles. Moreover, the genetic algorithm (GA) has been modified and applied to produce time-based trajectory for the optimal path. It is based on proposing and enhancing the searching ability of the robot to move towards the optimal path solution. Many scenarios have been adopted in indoor environment to verify the capability of the new algorithm in terms of detection efficiency and computational time.

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Section: Intelligent Image Processing Algorithm (Iipa)mentioning

confidence: 99%

A Novel Edge Detection Algorithm for Mobile Robot Path Planning

Al-Jarrah

Al‐Jarrah

Roth

2018

Journal of Robotics

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“…If the function v(t) lies in the sector [β, β + k], then using the substitution (7) we obtain the equivalent system (see "eigenvalues shifting" in [32], [33]) (9). Applying Theorem 1 for G 1 (s) we get the condition (12) in Theorem 2.…”

Section: Appendix a Proof Of Theoremmentioning

confidence: 99%

Design of State Feedback Adaptive Fuzzy Controllers for Second-Order Systems Using a Frequency Stability Criterion

Wiktorowicz

2017

IEEE Trans. Fuzzy Syst.

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This paper proposes a design method for secondorder systems that guarantees the stability of an adaptive fuzzy controller with state feedback. The system consists of a linear unstable plant, the Takagi-Sugeno controller, a nonlinear reference model and an adaptation mechanism. In this paper, gradient-based adaptation is used to change the consequents of the controller rules so that the closed-loop system behaves like the reference model. The proposed method utilizes a frequencydomain criterion in the form of the modified circle theorem. The controller function is assumed to be a nonlinearity described by a sector condition, which means that the function lies between two planes. During the process of adaptation this function is verified so it stays in the sector guaranteeing stability. The method described here is illustrated by an example of a control system containing an unstable plant with an unknown pole in the right half-plane. The motivation of this paper is to propose a frequency-domain method for the design of state feedback adaptive fuzzy controllers. Comparing with time domain methods that require advanced software tools, the proposed method offers simple graphical interpretation on the Nyquist plane.Index Terms-adaptive control, fuzzy control, nonlinear control systems, stability NOMENCLATURE r(t)Reference signal. v(t)Output of the controller. u(t) Control variable. x(t)State vector of the plant. x m (t)State vector of the reference model. ε(t) Adaptation error. A, BMatrices of the plant. A m , B m

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“…Fuzzy theory and fuzzy logic are well-known to be flexible tools where imprecise knowledge or not-well defined features have to be used [17]. It has been extensively used in system control domain and applied in image processing to some extent [18][19][20][21].…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Multilevel Image Thresholding Based on Modified Quick Artificial Bee Colony Algorithm and Local Information Aggregation

Sun

Guo

et al. 2016

Mathematical Problems in Engineering

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Thresholding segmentation based on fuzzy entropy and intelligent optimization is one of the most commonly used and direct methods. This paper takes fuzzy Kapur's entropy as the best optimal objective function, with modified quick artificial bee colony algorithm (MQABC) as the tool, performs fuzzy membership initialization operations through Pseudo Trapezoid-Shaped (PTS) membership function, and finally, according to the image's spacial location information, conducts local information aggregation by way of median, average, and iterative average so as to achieve the final segmentation. The experimental results show that the proposed FMQABC (fuzzy based modified quick artificial bee colony algorithm) and FMQABCA (fuzzy based modified quick artificial bee colony and aggregation algorithm) can search out the best optimal threshold very effectively, precisely, and speedily and in particular show exciting efficiency in running time. This paper experimentally compares the proposed method with Kapur's entropy-based Electromagnetism Optimization (EMO) method, standard ABC, and FDE (fuzzy entropy based differential evolution algorithm), respectively, and concludes that MQABCA is far more superior to the rest in terms of segmentation quality, iterations to convergence, and running time.

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Fuzzy sets for image processing and understanding

Cited by 73 publications

References 134 publications

A Novel Edge Detection Algorithm for Mobile Robot Path Planning

A Novel Edge Detection Algorithm for Mobile Robot Path Planning

Design of State Feedback Adaptive Fuzzy Controllers for Second-Order Systems Using a Frequency Stability Criterion

Fuzzy Multilevel Image Thresholding Based on Modified Quick Artificial Bee Colony Algorithm and Local Information Aggregation

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