scholarly journals Metric Induced Morphological Operators on Intuitionistic Fuzzy Hypergraphs

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
P. M. Dhanya ◽  
A. Sreekumar ◽  
M. Jathavedan ◽  
P. B. Ramkumar
Keyword(s):  
Image Processing ◽  
Network Analysis ◽  
Text Processing ◽  
Morphological Operators ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Hypergraphs ◽  
Processing Network ◽  
Sequential Filter

A hypergraph consisting of hyperedges and nodes can be made intuitionistic fuzzy hypergraph (IFHG) by assigning membership and nonmembership degrees for both nodes and edges. Just as a hypergraph, an IFHG is also having hyperedges consisting of many nodes. Many flavours of a given IFHG can be created by applying morphological operators like dilation, erosion, adjunction, etc. The focus of this paper is to define morphological adjunction, opening, closing, and Alternative Sequential Filter (ASF) on IFHG. The system modeled in this way finds application in text processing, image processing, network analysis, and many other areas.

Digital Color Image Processing Using Intuitionistic Fuzzy Hypergraphs

2021 ◽  
Vol 11 (3) ◽  
pp. 21-40
Author(s):  
Fateh Boutekkouk
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Digital Images ◽  
Color Image ◽  
Fuzzy Theory ◽  
Entropy Measure ◽  
Mathematical Tool ◽  
Intuitionistic Fuzzy ◽  
Intuitionistic Fuzzy Theory ◽  
Fuzzy Hypergraphs

Hypergraphs are considered a useful mathematical tool for digital image processing and analysis since they can represent digital images as complex relationships between pixels or block of pixels. The notion of hypergraphs has been extended in fuzzy theory leading to the concept of fuzzy hypergraphs, then in intuitionistic fuzzy theory conducting to the concept of intuitionistic fuzzy hypergraphs or IFHG. The latter is very suitable to model digital images with uncertain or imprecise knowledge. This paper deals with color image denoising, segmentation, and edge detection in a color image initially represented in RGB space using intuitionistic fuzzy hypergraphs. First, the RGB image is transformed to HLS space resulting in three separated components. Then each component is intuitionistically fuzzified based on entropy measure from which an intuitionistic fuzzy hypergraph is generated automatically. The generated hypergraphs will be used for denoising, segmentation, and edge detection.

Color Image Processing Under Uncertainty

2021 ◽  
Vol 12 (2) ◽  
pp. 46-67
Author(s):  
Fateh Boutekkouk ◽  
Narimane Sahel
Keyword(s):  
Image Processing ◽  
Digital Images ◽  
Color Image ◽  
Color Image Segmentation ◽  
Entropy Measure ◽  
Mathematical Tool ◽  
Intuitionistic Fuzzy ◽  
Lighting Conditions ◽  
Complex Relationships ◽  
Fuzzy Hypergraphs

Most digital images have uncertainties associated with the intensity levels of pixels and/or edges. These uncertainties can be traced back to the acquisition chain, to uneven lighting conditions used during imaging or to the noisy environment. On the other hand, intuitionistic fuzzy hypergraphs are considered a useful mathematical tool for digital image processing since they can represent digital images as complex relationships between pixels and model uncertain or imprecise knowledge explicitly. This paper presents the approach for noisy color image segmentation and edge detection based on intuitionistic fuzzy hypergraphs. First, the RGB image is transformed to the HLS space resulting in three separated components. Then each component is intuitionistically fuzzified based on entropy measure from which an intuitionistic fuzzy hypergraph is generated automatically. The generated hypergraphs will be used for denoising, segmentation, and edges detection. The first experimentations showed that the proposed approach gave good results especially in the case of dynamic threshold.

An investigation into colour combination in paintings via graph theory

2019 ◽  
Vol 8 (4) ◽  
Author(s):  
Samaneh Jolany Vangah ◽  
Yousef Jamali ◽  
Mozaffar Jamali
Keyword(s):  
Image Processing ◽  
Graph Theory ◽  
Network Analysis ◽  
Visual Arts ◽  
Digital Image ◽  
Degree Distribution ◽  
Network Science ◽  
Node Centrality ◽  
Classification Of Images

Abstract In visual arts, painting is deeply reliant on the colour combination for its impact, depth and emotion. Recently, many studies have focused on image processing, regarding identification and classification of images, using some colour features such as saturation, hue, luminance and so forth. This study aims to delve into some of the painting styles from the perspective of graph theory and network science. We compared a number of famous paintings to find out the likely pattern that an artist uses for colour combination and juxtaposition. To achieve this aim, the digital image of a painting is converted to a graph where each vertex represents one of the painting’s colours. In this graph, two vertices would be adjacent if and only if the two relative colours could be found in at least two adjacent pixels in the digital image. Among the several tools for network analysis, clustering, node centrality and degree distribution are used. Outcomes showed that artists unconsciously are following subtle mathematical rules to reach harmony and coordination in their work.

Image Processing for Electron Microscope Investigations of Materials

1983 ◽  
Vol 31 ◽  
Author(s):  
William Krakow
Keyword(s):  
Image Processing ◽  
Text Processing ◽  
Computer Control ◽  
Full Range ◽  
Computer Software ◽  
Processing System ◽  
Image Motion ◽  
Hard Copy ◽  
Digital Television ◽  
Main Component

ABSTRACTA time shared television digital image processing system has been developed for on-line electron microscopy and uses a large mainframe computer. The main component of the system is a digital television frame store which has many standard features for digital analysis such as: digitization, zoom and pan, arithmetic and Boolean processors, alphanumeric generators and so on. Images can be acquired at atomic resolution from a TEM, analyzed in real time and hard copy slides made under full computer control. A full range of computer software has been developed or modified from existing software and is generally compatible with IBM Fortran compilers. Some of the areas where extensive menu driven software has been developed are: particle size and feature analysis, algebraic and geometric image manipulations, Fourier analysis, digitization and process control, image contrast correction, text processing, etc. A number of applications areas have been explored which include: the structure of Si/SiO2interfaces; nucleation of Au on rocksalt; the formation of hexatic structures from amorphous phases under shear, tension and compression; analysis of atomic surface structure and image motion and the analysis of field ion micrographs of amorphous structures. Several of these areas will be discussed in the context of image processing and materials characterization.

scholarly journals New Windows based Color Morphological Operators for Biomedical Image Processing

2016 ◽  
Vol 705 ◽  
pp. 012023 ◽  
Author(s):  
Juan Pastore ◽  
Agustina Bouchet ◽  
Marcel Brun ◽  
Virginia Ballarin
Keyword(s):  
Image Processing ◽  
Biomedical Image Processing ◽  
Morphological Operators ◽  
Biomedical Image

Image processing based vascular network analysis

2015 ◽  
Author(s):  
Sercan Erbakan ◽  
Ibrahim Sefa Bakar ◽  
Erkan Zeki Engin ◽  
Kubra Seker ◽  
Ibrahim Akkaya ◽  
...  
Keyword(s):  
Image Processing ◽  
Network Analysis ◽  
Vascular Network
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