Hyperspectral imaging using notions from type-2 fuzzy sets

López-Maestresalas, Ainara; Miguel, Laura De; López-Molina, Carlos; Arazuri, Silvia; Bustince, Humberto; Jarén, Carmen

doi:10.1007/s00500-018-3208-8

Cited by 3 publications

(2 citation statements)

References 74 publications

(84 reference statements)

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“…The term I T represents the set of images with a certain tonal palette T. For example, I {0,1} represents the set of binary images, while I [0,1] is the set of real-valued grayscale images. More complex instantiations of T could be, for example, hyperspectral signatures [15]. Definition 1 (Inclusion of binary images).…”

Section: A Framework For Active Contour Model Initializationmentioning

confidence: 99%

A Framework for Active Contour Initialization with Application to Liver Segmentation in MRI

Mir-Fuentes,

Mir,

Antunes-Santos

et al. 2022

Information Processing and Management of Uncertainty in Knowledge-Based Systems

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Object segmentation is a prominent low-level task in image processing and computer vision. A technique of special relevance within segmentation algorithms is active contour modeling. An active contour is a closed contour on an image which can be evolved to progressively fit the silhouette of certain area or object. Active contours shall be initialized as a closed contour at some position of the image, further evolving to precisely fit to the silhouette of the object of interest. While the evolution of the contour has been deeply studied in literature [11,5], the study of strategies to define the initial location of the contour is rather absent from it. Typically, such contour is created as a small closed curve around an inner position in the object. However, literature contains no generalpurpose algorithms to determine those inner positions, or to quantify their fitness. In fact, such points are frequently set manually by human experts, hence turning the segmentation process into a semi-supervised one. In this work, we present a method to find inner points in relevant object using spatial-tonal fuzzy clustering. Our proposal intends to detect dominant clusters of bright pixels, which are further used to identify candidate points or regions around which active contours can be initialized.

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Section: A Framework For Active Contour Model Initializationmentioning

confidence: 99%

A Framework for Active Contour Initialization with Application to Liver Segmentation in MRI

Mir-Fuentes,

Mir,

Antunes-Santos

et al. 2022

Information Processing and Management of Uncertainty in Knowledge-Based Systems

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“…Therefore, the use of type-2 fuzzy near sets seems suitable for the paper context. Actually, the image retrieval by using Interval Type-2 Fuzzy Logic has been proven to be a great success in a large variety of applications, such as [27]- [29], among the works can find some: Xing and al. in [30] have been proposed an interval type-2 fuzzy clustering method based on neighborhood information to improve the classification accuracy of remote sensing images with complex land cover.…”

Section: Introductionmentioning

confidence: 99%

Interval Type-2 Beta Fuzzy Near Sets Approach to Content-Based Image Retrieval

Ghozzi

Baklouti

Hagras

et al. 2022

IEEE Trans. Fuzzy Syst.

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In computer-based search systems, similarity plays a key role in replicating the human search process which underlies many natural abilities, such as image recovery, language comprehension, decision making, or pattern recognition. The search for images consists in establishing a correspondence between the available images and those sought by the user, by measuring the similarity between the images. In fact, image search per content is generally based on the similarity between the visual characteristics of the images. The distance function used to evaluate the similarity between images depends notonly on the criteria of the search but also on the representation of the characteristics of the image. This is the main idea of a contentbased image retrieval (CBIR) system. In this article, we first constructed type-2 beta fuzzy membership of descriptor vectors to help manage inaccuracy and uncertainty of the characteristics extracted from the feature of images. Subsequently, the retrieved images are ranked according to the novel similarity measure, which is noted type-2 fuzzy nearness measure (IT2FNM). By analogy to Type-2 Fuzzy Logic, and motivated by a near sets theory, we advanced a new fuzzy similarity measure (FSM) noted as interval type-2 fuzzy nearness measure (IT-2 FNM). Then, we proposed three new IT-2 FSMs and provided mathematical justification to demonstrate that the proposed FSMs satisfy proximity properties (i.e. reflexivity, transitivity, symmetry, and overlapping). The experimental results generated using three image databases show consistent and significant results.

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