The Segmentation of Ferrography Images: A Brief Survey

2013 ◽  
Vol 770 ◽  
pp. 427-432 ◽  
Author(s):  
Jing Qiu Wang ◽  
Xiao Lei Wang
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Particle Image ◽  
Wear Particle ◽  
Wear Particles ◽  
Clustering Methods ◽  
General Overview ◽  
Improved Methods ◽  
Traditional Image

This paper provides a general overview on the developments and progress in the segmentation of ferrography images. The problems experienced with applying traditional image processing methods in the segmentation of wear particles, revealed that it is still a big challenge for intelligent ferrography. This has highlighted the need for combining the segmentation and clustering methods for performing ferrography image analysis. In this paper, some of the developments reported in the literature relating to progress made with wear particle image segmentation are reported and examined as a basis for establishing improved methods of ferrography image analysis.

A Survey on Solar Image Segmentation Techniques

2014 ◽  
Vol 945-949 ◽  
pp. 1899-1902
Author(s):  
Yuan Yuan Fan ◽  
Wei Jiang Li ◽  
Feng Wang
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Image Analysis ◽  
Image Segmentation ◽  
Image Retrieval ◽  
Fundamental Issue ◽  
Solar Image ◽  
Segmentation Methods

Image segmentation is one of the basic problems of image processing, also is the first essential and fundamental issue in the solar image analysis and pattern recognition. This paper summarizes systematically on the image segmentation techniques in the solar image retrieval and the recent applications of image segmentation. Then the merits and demerits of each method are discussed in this paper, in this way we can combine some methods for image segmentation to reach the better effects in astronomy. Finally, according to the characteristics of the solar image itself, the more appropriate image segmentation methods are summed up, and some remarks on the prospects and development of image segmentation are presented.

A Critical Overview of Image Segmentation Techniques Based on Transition Region

2018 ◽  
pp. 1308-1318
Author(s):  
Yu-Jin Zhang
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Transition Region ◽  
Google Scholar ◽  
Research Directions ◽  
Distinctive Type ◽  
Critical Overview

Image segmentation is the key step from image processing to image analysis, and is an important technique of image engineering. Image segmentation based on transition region is a special or distinctive type of techniques that are different from traditional boundary-based or region-based techniques. Since the first technique using transition region proposed, there are many subsequent related researches and applications, and a series of papers in the literature citing are published worldwide. Using Google Scholar, a number of papers citing the original papers are searched, a study on the statistics of these papers is conducted. These papers are sorted first according to the publishing year, and then grouped according to their purposes and contents (with techniques used). Some questionable issues in these papers are pointed out and critically discussed, and several further research directions are indicated and analyzed.

Fuzzy Clustering-Based Approaches in Automatic Lip Segmentation from Color Images

2006 ◽  
pp. 292-317
Author(s):  
Shilin Wang ◽  
Wing Hong Lau ◽  
Alan Wee-Chung Liew ◽  
Shu Hung Leung
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Visual Information ◽  
Spatial Information ◽  
Noisy Environments ◽  
Low Contrast ◽  
Speaker Authentication ◽  
Lip Segmentation ◽  
Traditional Image

Recently, lip image analysis has received much attention because the visual information extracted has been shown to provide significant improvement for speech recognition and speaker authentication, especially in noisy environments. Lip image segmentation plays an important role in lip image analysis. This chapter will describe different lip image segmentation techniques, with emphasis on segmenting color lip images. In addition to providing a review of different approaches, we will describe in detail the state-of-the-art classification-based techniques recently proposed by our group for color lip segmentation: “Spatial fuzzy c-mean clustering” (SFCM) and “fuzzy c-means with shape function” (FCMS). These methods integrate the color information along with different kinds of spatial information into a fuzzy clustering structure and demonstrate superiority in segmenting color lip images with natural low contrast in comparison with many traditional image segmentation techniques.

Review of Otsu Segmentation Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 1959-1961 ◽  
Author(s):  
Yan Xue Dong
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Simple Calculation ◽  
Segmentation Algorithm ◽  
Image Thresholding ◽  
Otsu Method

Image segmentation is the key step in the process from image processing to image analysis. Otsu method is one of the most successful methods for image thresholding because of its simple calculation. Otsu method can select threshold automatically and divide the object from the background in the image. In this paper, various Otsu algorithm are studied.

scholarly journals A Study on Image Segmentation Techniques

2019 ◽  
Vol 8 (S2) ◽  
pp. 75-78
Author(s):  
S. Abdul Saleem ◽  
G. Vinitha
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Color Image ◽  
Video Frame ◽  
Challenging Problem ◽  
Digital Form ◽  
Low Level ◽  
Grey Scale ◽  
High Level

Image processing is a technique to transform an image into digital form and implement some operations on it; in order to acquire an improved image or to abstract some useful information from it. It is a kind of signal exemption in which input is image, like video frame or photograph and output may be image or characteristics related with that image. Segmentation partitions an image into separate regions comprising each pixel with similar attributes. To be significant and useful for image analysis and clarification, the regions should powerfully relate to depicted objects or features of interest. Meaningful segmentation is the first step from low-level image processing converting a grey scale or color image into one or more other images to high-level image depiction in terms of objects, features, and scenes. The achievement of image analysis depends on reliability of segmentation, but an exact partitioning of an image is mostly a very challenging problem.

Study of Relative Entropy Coefficients for Image Segmentation Based on Particle Swarm Optimization

2011 ◽  
Vol 58-60 ◽  
pp. 1056-1060
Author(s):  
You Rui Huang ◽  
Li Guo Qu
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Relative Entropy ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Threshold Method ◽  
Swarm Optimization ◽  
Computation Algorithm

Image segmentation is the basis of image analysis, and because of its simplicity, rapidity and stability, the threshold method is the important one, applying in the image processing and recognition widely. In this paper, a new method is proposed, which based on relative entropy coefficients between random variables. It maximizes the target and background, which is the relative entropy coefficient in probability distribution, and gets the optimal threshold of image segmentation, and then optimizes it using particle swarm algorithm which is an evolutionary computation algorithm. The result of relative entropy coefficients for image segmentation proves its feasibility and better effect.

Measurement of Critical Dimensions of Cold Roll Formed Sections Using Digital Image Processing

2011 ◽  
Vol 473 ◽  
pp. 949-956 ◽  
Author(s):  
Ian Williams ◽  
Bez Shirvani ◽  
Jean Michael Mourier
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Roll Forming ◽  
Critical Dimensions ◽  
Knowledge Based ◽  
Cold Roll ◽  
Steel Sections ◽  
Manual Processing ◽  
Traditional Image

A research investigation is presented which discusses the practicality of using several image processing and knowledge based techniques for the measurement and classification of cold rolled steel sections. Image analysis techniques can be applied to many different applications and assessing the quality and the accuracy of cold roll formed steel sections is no exception. The operations detailed within this paper are both traditional image processing methods and novel neural network based techniques which are combined together to give a bespoke alternative to the manual processing currently employed to test these sections. The results show the suitability of using image analysis and image processing to aid in the quality control of cold steel roll forming and initial tests have demonstrated great potential for this work.

scholarly journals The Segmentation of Wear Particles Images UsingJ-Segmentation Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Hong Liu ◽  
Haijun Wei ◽  
Lidui Wei ◽  
Jingming Li ◽  
Zhiyuan Yang
Keyword(s):  
Image Segmentation ◽  
Classification System ◽  
Color Image ◽  
Promising Result ◽  
Wear Particle ◽  
Identification System ◽  
Wear Particles ◽  
Edge Detector ◽  
Computing Complexity ◽  
Particle Segmentation

This study aims to use a JSEG algorithm to segment the wear particle’s image. Wear particles provide detailed information about the wear processes taking place between mechanical components. Autosegmentation of their images is key to intelligent classification system. This study examined whether this algorithm can be used in particles’ image segmentation. Different scales have been tested. Compared with traditional thresholding along with edge detector, the JSEG algorithm showed promising result. It offers a relatively higher accuracy and can be used on color image instead of gray image with little computing complexity. A conclusion can be drawn that the JSEG method is suited for imaged wear particle segmentation and can be put into practical use in wear particle’s identification system.

scholarly journals Brain Tumor Segmentation using Multi Level Thresholding using Fuzzy Entropy

2020 ◽  
Vol 8 (5) ◽  
pp. 2641-2643
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Brain Tumor ◽  
Fuzzy Entropy ◽  
Image Processing Technique ◽  
Tumor Segmentation ◽  
Fuzzy Approach ◽  
Segmentation Process ◽  
Multi Level

In image processing field, image processing technique is used to distinguish the object from its image scene at pixel level. The image segmentation process is the significant task in the processing of image field as well as in image analysis. The most difficult task in the image analysis field is the automatic separation of object from its background. To alleviate this problem several image segmentation process is introduced are gray level thresholding, edge detection method, interactive pixel classification method, neural network approach and segmentation based on fuzzy approach This chapter presents the multilevel set thresholding method using partition of fuzzy approach on brain image histogram and theory of entropy. The fuzzy entropy method is applied on multi-level brain tumor MRI image segmentation method. The threshold of brain MR image is obtained by optimized the entropy measure. In this method, Differential Evolution technique is used to find the best solution.

The Study of Three-Dimensional Analysis Techniques and Automatic Classification Systems for Wear Particles

1999 ◽  
Vol 121 (1) ◽  
pp. 169-176 ◽  
Author(s):  
Z. Peng ◽  
T. B. Kirk
Keyword(s):  
Image Analysis ◽  
Wear Debris ◽  
Three Dimensional ◽  
Wear Particle ◽  
Automatic Classification ◽  
Classification Systems ◽  
Image Analysis System ◽  
Wear Particles ◽  
Machine Condition ◽  
Analysis System

Although the morphology of wear debris generated in a machine has a direct relationship to wear processes and machine condition, studying wear particles for machine condition monitoring has not been widely applied in Industry as it is time consuming and requires certain expertise of analysts. To overcome these obstacles, automatic wear particle analysis and identification systems need to be developed. In this paper, laser scanning confocal microscopy has been used to obtain three-dimensional images of metallic wear particles. An analysis system has been developed and applied to study the boundary morphology and surface topography of the wear debris. After conducting the image analysis procedure and selecting critical criteria from dozens of available parameters, neural networks and grey systems have been investigated to classify unknown patterns using the numerical descriptors. It is demonstrated that the combination of the image analysis system and automatic classification systems has provided an automatic package for wear particle study which may be applied to industrial applications in the future.

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