2D digital image segmentation techniques to characterize the porosity of carbonate rocks in Brazilian pre-salt

2021 ◽  
Author(s):  
H. Rocha ◽  
A. Carrasquila
Keyword(s):  
Image Segmentation ◽  
Digital Image ◽  
Carbonate Rocks

scholarly journals Techniques for Image Segmentation: A Critical Review

2021 ◽  
Vol 9 (3) ◽  
pp. 1-4
Author(s):  
Harshita Mishra ◽  
Anuradha Misra
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Image Segmentation ◽  
Digital Image ◽  
Electronic Devices ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Mathematical Algorithms ◽  
Picture Element

In today’s world there is requirement of some techniques or methods that will be helpful for retrieval of the information from the images. Information those are important for finding solution to the problems in the present time are needed. In this review we will study the processing involved in the digitalization of the image. The set or proper array of the pixels that is also called as picture element is known as image. The positioning of these pixels is in matrix which is formed in columns and rows. The image undergoes the process of digitalization by which a digital image is formed. This process of digitalization is called digital image processing of the image (D.I.P). Electronic devices as such computers are used for the processing of the image into digital image. There are various techniques that are used for image segmentation process. In this review we will also try to understand the involvement of data mining for the extraction of the information from the image. The process of the identifying patterns in the large stored data with the help of statistic and mathematical algorithms is data mining. The pixel wise classification of the image segmentation uses data mining technique.

Theoretical Concepts and Technical Aspects on Image Segmentation

2018 ◽  
pp. 2333-2348
Author(s):  
Anju Pankaj ◽  
Sonal Ayyappan
Keyword(s):  
Image Segmentation ◽  
Digital Image ◽  
High Variability ◽  
Technical Aspects ◽  
Theoretical Concepts ◽  
Detailed Assessment

Image segmentation is the process of partitioning a digital image into multiple segments (super pixels). Segmentation is typically used to locate objects and boundaries in images. The result of segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image. Each of the pixels in a region is similar with respect to some characteristic or computed property. Adjacent regions are significantly different with respect to the same characteristics. A predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image is defined. An important characteristic of the method is its ability to preserve detail in low-variability image regions and ignoring detail in high variability regions. This chapter discuss basic aspects of segmentation and an application and presents a detailed assessment on different methods in image segmentation and discusses a case study on it.

A Study on Different Edge Detection Techniques in Digital Image Processing

2017 ◽  
pp. 100-122 ◽  
Author(s):  
Shouvik Chakraborty ◽  
Mousomi Roy ◽  
Sirshendu Hore
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Basic Feature ◽  
Detection Methods ◽  
Natural Image ◽  
Detection Techniques ◽  
Image Edges

Image segmentation is one of the fundamental problems in image processing. In digital image processing, there are many image segmentation techniques. One of the most important techniques is Edge detection techniques for natural image segmentation. Edge is a one of the basic feature of an image. Edge detection can be used as a fundamental tool for image segmentation. Edge detection methods transform original images into edge images benefits from the changes of grey tones in the image. The image edges include a good number of rich information that is very significant for obtaining the image characteristic by object recognition and analyzing the image. In a gray scale image, the edge is a local feature that, within a neighborhood, separates two regions, in each of which the gray level is more or less uniform with different values on the two sides of the edge. In this paper, the main objective is to study the theory of edge detection for image segmentation using various computing approaches.

scholarly journals Digital image segmentation for foaminess measurement on foam stability of the lubricant products

2019 ◽  
Vol 1171 ◽  
pp. 012029 ◽  
Author(s):  
R T Yunardi ◽  
A A Rizqi ◽  
R N Naufal ◽  
F C S Arisgraha
Keyword(s):  
Image Segmentation ◽  
Digital Image ◽  
Foam Stability

scholarly journals Detection of Surface Defects on Steel Strips Based on Singular Value Decomposition of Digital Image

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Qianlai Sun ◽  
Jianghui Cai ◽  
Zhiyi Sun
Keyword(s):  
Image Segmentation ◽  
Singular Value Decomposition ◽  
Digital Image ◽  
Manufacturing Industry ◽  
Surface Defects ◽  
Sudden Change ◽  
Singular Value ◽  
Steel Strips ◽  
Novel Method ◽  
Value Decomposition

Image segmentation technology has been widely used to detect the surface defects in metal industries effectively. In some fields of the manufacturing industry, the determination of defects is more concerned than the accurate location and shape of defects. However, most of current image segmentation algorithms are complex or have difficulty determining the defect. This paper presents a novel method for determining and roughly locating the surface defects of steel strips based on Singular Value Decomposition. The method has no need of image segmentation. The gray level matrix of a digital image is projected on its singular vectors obtained by Singular Value Decomposition. A defect is reflected as a sudden change on the projections. Therefore, the defects can be determined and roughly located according to the sudden changes. The experimental results suggest that this method is valid and convenient for determining the surface defects directly.

On Khalimsky topology and applications on the digital image segmentation

2015 ◽  
Vol 9 ◽  
pp. 3687-3701 ◽  
Author(s):  
M. Al Hajri ◽  
K. Belaid ◽  
L. Jaafar Belaid
Keyword(s):  
Image Segmentation ◽  
Digital Image ◽  
Khalimsky Topology
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