Review of Recent Type-2 Fuzzy Image Processing Applications

Castillo, Oscar; Sánchez, Martínez; González, Claudia I.; Martínez, Gonzalo

doi:10.3390/info8030097

Cited by 51 publications

(19 citation statements)

References 53 publications

(104 reference statements)

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“…Fuzzy logic has also been used for edge sharpening as described in [54]. Fuzzy logic can also be employed in other stages like the segmentation and classification as will be described in the following sections.…”

Section: Image Enhancement Filtering Methodsmentioning

confidence: 99%

“…The drawback of a smoothing technique is that the edges may be blurred and thus, it may be difficult to accurately determine the borders e.g., of a lesion or a human organ. Edge sharpening techniques can be employed for reassuring that the edges will remain distinct [54]. The contrast may also be increased for more precise mapping of the ROIs.…”

Section: Image Enhancement Filtering Methodsmentioning

confidence: 99%

“…The constant m ≥ 1 controls the fuzziness of the classification process and FCM approximates the k-means hard decision algorithm when m = 1. Several other fuzzy logic methods for image segmentation are described in [54].…”

Section: Segmentation Methodsmentioning

confidence: 99%

“…Fat in the liver can be recognized as described in [53] where images are captured from a microscope during biopsy. The use of fuzzy logic in medical image processing applications is reviewed in [54].…”

Section: Introductionmentioning

confidence: 99%

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A Review of Image Processing Techniques Common in Human and Plant Disease Diagnosis

Petrellis

2018

Symmetry

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Image processing has been extensively used in various (human, animal, plant) disease diagnosis approaches, assisting experts to select the right treatment. It has been applied to both images captured from cameras of visible light and from equipment that captures information in invisible wavelengths (magnetic/ultrasonic sensors, microscopes, etc.). In most of the referenced diagnosis applications, the image is enhanced by various filtering methods and segmentation follows isolating the regions of interest. Classification of the input image is performed at the final stage. The disease diagnosis approaches based on these steps and the common methods are described. The features extracted from a plant/skin disease diagnosis framework developed by the author are used here to demonstrate various techniques adopted in the literature. The various metrics along with the available experimental conditions and results presented in the referenced approaches are also discussed. The accuracy achieved in the diagnosis methods that are based on image processing is often higher than 90%. The motivation for this review is to highlight the most common and efficient methods that have been employed in various disease diagnosis approaches and suggest how they can be used in similar or different applications.

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Section: Image Enhancement Filtering Methodsmentioning

confidence: 99%

Section: Image Enhancement Filtering Methodsmentioning

confidence: 99%

Section: Segmentation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Review of Image Processing Techniques Common in Human and Plant Disease Diagnosis

Petrellis

2018

Symmetry

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“…The authors show that the proposed method based on a general type-2 fuzzy system outperforms the other methods because of its ability to handle the intrinsic uncertainty in the edge detection problem. In the review paper "Review of Recent Type-2 Fuzzy Image Processing Applications" [6], a representative and concise review of type-2 fuzzy logic applications in image segmentation, image filtering, image classification and edge detection is presented. The authors envision that, in the future, the number of works using type-2 fuzzy logic in image processing will increase, as even more complex real-world applications would require this kind of higher type models.…”

mentioning

confidence: 99%

Special Issue on Fuzzy Logic for Image Processing

Caponetti

Castellano

2017

Information

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The increasing availability of huge image collections in different application fields, such as medical diagnosis, remote sensing, transmission and encoding, machine/robot vision, and video processing, microscopic imaging has pressed the need, in the last few last years, for the development of efficient techniques capable of managing and processing large collection of image data. In particular, techniques suitable for analysis, indexing and the retrieval of image data are of fundamental importance today. Classical image processing methods often face great difficulties while dealing with images containing noise and distortions. Under such conditions, fuzzy logic techniques turn out to be effective to address challenging real-world image processing problems that are often characterized by vagueness and uncertainty. The present Special Issue on Fuzzy Logic for Image Processing is intended to show the potential and the practical impacts of fuzzy logic techniques in challenging applications involving tasks required to understand, represent, and process digital images.The Special Issue received several submissions, all of which went through a rigorous peer-review process. After the review process, six papers have been selected on the basis of the review ratings and comments. These selected papers range over main applications of fuzzy logic in image processing, including image classification, image segmentation and edge detection.Classification of scenes, regions or objects within images is a fundamental task in many fields, especially related to image retrieval and remote sensing. In particular, scene classification in remote sensing images is an active research topic in the field of aerial and satellite image analysis, which aims to is to categorize images into a discrete set of meaningful classes according to the region types on the Earth's surface. The first paper entitled "Land Cover Classification from Multispectral Data Using Computational Intelligence Tools: A Comparative Study" [1] addresses the problem of image fusion in land cover classification from multispectral satellite images. The authors propose a fuzzy fusion approach to fuse remote sensing spectral images into a higher level image of land cover distribution. When compared against other computational intelligence methods, this fuzzy approach demonstrates its suitability for spatio-temporal image fusion.Another essential and crucial task in image processing is segmentation, which consists of identifying homogeneous regions of interest in images for facilitating their characterization and further processing. Particularly, image segmentation is of fundamental importance in the field of medical imaging. For example, in Magnetic Resonance (MR) brain image analysis, segmentation is commonly used for detecting, measuring and analyzing the main anatomical structures of the brain and eventually identifying pathological regions. Among image segmentation methods, clustering-based approaches received a great interest in the domain of medical imaging and several fu...

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Trajectory tracking control of underactuated tendon‐driven truss‐like manipulator based on type‐1 and interval type‐2 fuzzy logic approach

Ding

Wen

et al. 2021

Int J of Intelligent Sys

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This paper deals with the trajectory tracking control problem of a planar underactuated tendon‐driven truss‐like manipulator (UTTM) by using fuzzy logic control methods, including type‐1 fuzzy and interval type‐2 fuzzy approaches. The UTTM is a novel designed underactuated robot that excels in manipulating in harsh environments due to its tendon‐driven parallelogram structure. This unique structure, however, poses challenges for UTTM's trajectory tracking controller design, including underactuation, excessive nonlinearity, parameter uncertainty, and so on. To solve these problems, a fuzzy logic‐based approach is proposed. First, the dynamic model of UTTM is transformed into a partially linearized form. Then a type‐1 fuzzy controller is designed according to a linear quadratic regulator controller for the linearized system. Then by blurring the membership functions of type‐1 fuzzy controllers, an interval type‐2 fuzzy logic controller is designed, aiming at dealing with uncertainties. The proposed type‐1 and interval type‐2 fuzzy logic controllers are validated through simulations, and made comparisons with each other, especially when the system is involved with uncertainties. Simulation results show that the interval type‐2 fuzzy‐based trajectory tracking controller provides better performance than the type‐1 counterpart in terms of tracking accuracy and capacity against uncertainties.

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Review of Recent Type-2 Fuzzy Image Processing Applications

Cited by 51 publications

References 53 publications

A Review of Image Processing Techniques Common in Human and Plant Disease Diagnosis

A Review of Image Processing Techniques Common in Human and Plant Disease Diagnosis

Special Issue on Fuzzy Logic for Image Processing

Trajectory tracking control of underactuated tendon‐driven truss‐like manipulator based on type‐1 and interval type‐2 fuzzy logic approach

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