An improved sobel edge detection method based on generalized type-2 fuzzy logic

González, Claudia I.; Melín, Patricia; Castro, Juan R.; Mendoza, Olivia; Castillo, Oscar

doi:10.1007/s00500-014-1541-0

Cited by 171 publications

(43 citation statements)

References 36 publications

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“…All papers use real images and the benchmark images database for edge detection, with the exception of papers [41,44], which only use synthetic images to evaluate performance. In general, edge detection accuracy is validated using frequency histograms, visual appreciation, figure of merit of Pratt, percentage correct classification, Kappa statistic and overall error measures.…”

Section: Discussionmentioning

confidence: 99%

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Review of Recent Type-2 Fuzzy Image Processing Applications

et al. 2017

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This paper presents a literature review of applications using type-2 fuzzy systems in the area of image processing. Over the last years, there has been a significant increase in research on higher-order forms of fuzzy logic; in particular, the use of interval type-2 fuzzy sets and general type-2 fuzzy sets. The idea of making use of higher orders, or types, of fuzzy logic is to capture and represent uncertainty that is more complex. This paper is focused on image processing systems, which includes image segmentation, image filtering, image classification and edge detection. Various applications are presented where general type-2 fuzzy sets, interval type-2 fuzzy sets, and interval-value fuzzy sets are used; some are compared with the traditional type-1 fuzzy sets and others methodologies that exist in the literature for these areas in image processing. In all accounts, it is shown that type-2 fuzzy sets outperform both traditional image processing techniques as well as techniques using type-1 fuzzy sets, and provide the ability to handle uncertainty when the image is corrupted by noise.

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Section: Discussionmentioning

confidence: 99%

“…Gonzalez et al (2016) [41], an improved Sobel edge detection method based on GT2 FS was presented. According to this paper, the authors presented in the same work the approach based on IT2 FS and GT2 FS; both fuzzy systems were designed using two inputs and one output.…”

Section: T2 Fs In Edge Detectionmentioning

confidence: 99%

Review of Recent Type-2 Fuzzy Image Processing Applications

et al. 2017

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“…In addition, the tools of fuzzy based edge detection have been described in [20]. In [21], an edge detection method has been proposed based on Sobel algorithm and the generalized type 2 fuzzy logic systems. In addition, to limit the complexity of handling the generalized type 2 fuzzy logic, -planes theory has been applied [21].…”

Section: Introductionmentioning

confidence: 99%

“…In [21], an edge detection method has been proposed based on Sobel algorithm and the generalized type 2 fuzzy logic systems. In addition, to limit the complexity of handling the generalized type 2 fuzzy logic, -planes theory has been applied [21]. Simulation results have been obtained with the Sobel operator and the generalized type 2 fuzzy logic system.…”

Section: Introductionmentioning

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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“…In order to reduce the complexity of general type-2 systems, they utilize the α-planes theory. Classical Sobel, type-1 fuzzy, interval type-2, and generalized type-2 system are compared to show advantages of method [14]. In this paper, nonstationary fuzzy sets are utilized both to avoid of complexity of general type-2 sets and to ensure the change in secondary memberships that interval type-2 sets are not capable.…”

Section: Introductionmentioning

confidence: 99%

Nonstationary Fuzzy Systems for Modelling and Control in Cyber Physical Systems under Uncertainty

Yetiş

Karaköse²

2017

ijisae

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Abstract:The applications of cyber-physical systems (CPS), which have a wide range from industrial to medical, are increasing day by day thanks to its reliable, scalable and flexible structure. In a CPS, the consistency and reliability of system are much more important, because they are generally used in large-scale and critical tasks. Uncertainties are unexpected situations and no matter how well a system designed they are a threat to a system always. Fuzzy logic is one of the algorithms that can be utilized in cyber layer easily. But because of its insufficiency in handling uncertainties new fuzzy types are emerged. Nonstationary fuzzy system is a type of fuzzy logic which is able to handle uncertainty in reasonable time. In this study a new inference system for nonstationary fuzzy systems is developed to enhance nonstationary fuzzy systems. The system is based on two main steps, first adding some random uncertainties to nonstationary inputs, and second obtaining single output value for the inputs. Thus, the fuzzy system always has uncertainty and the behavior of system is prepared for the uncertainties. The proposed method is verified by simulation results which demonstrate the effectiveness of system especially for noisy data compared to the type-1, and nonstationary fuzzy systems. The proposed method can be used in CPS which need consistency and robustness.

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An improved sobel edge detection method based on generalized type-2 fuzzy logic

Cited by 171 publications

References 36 publications

Review of Recent Type-2 Fuzzy Image Processing Applications

Review of Recent Type-2 Fuzzy Image Processing Applications

A Novel Edge Detection Algorithm for Mobile Robot Path Planning

Nonstationary Fuzzy Systems for Modelling and Control in Cyber Physical Systems under Uncertainty

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