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.

scholarly journals Research on color image segmentation

2018 ◽  
Vol 25 (03) ◽  
pp. 138-143
Author(s):  
Wang He Xi Ge Tu ◽  
Bolormaa D
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Digital Image Processing ◽  
Color Image ◽  
Color Image Segmentation ◽  
Primary Analysis ◽  
Programming Tools ◽  
Segmentation Methods ◽  
Segmentation Image

The basic foundation for the development of the image processing is image segments. Primary analysis, such as analysis of images and visualization of images, begins with segmentation. Image segmentation is one of the important parts of digital image processing. Depending on the accuracy and accuracy of the segmentation, the results of the image analysis, including the size of the object, the size of the object, and so on. In the first section of this study, briefly describe the types of image segments. Also use Mathlab language's powerful modern programming tools to explore the image segmentation methods and compare the results. As a result of the experiment, it is more accurate to accurately measure the trajectory of the image segmentation of the image as a result of the Otsu-based method of B space. This will apply to further research. Өнгөний мэдээлэлд суурилсан дүрс сегментчлэх аргын судалгаа Хураангуй: Дүрс боловсруулах судалгааны ажлын үндсэн суурь нь дүрс сегментчлэл юм. Дүрсэнд анализ хийх, дүрсийг ойлгох зэрэг анхан шатны боловсруулалт нь дүрс сегментчлэхээс эхэлдэг. Дүрс сегментчлэл нь дижитал дүрс боловсруулалтын чухал хэсгүүдийн нэг юм. Сегментчлэлийг хэр зэрэг үнэн зөв, нарийвчлал сайтай хийснээс шалтгаалан, дараагийн дүрс таних, обьектын хэмжээ зэрэг дүрс шинжлэлийн алхамын үр дүн ихээхэн хамаардаг. Энэхүү судалгааны ажлын эхний хэсэгт дүрс сегментчлэх арга төрлүүдийн талаар товч танилцуулна. Мөн орчин үеийн програмчлалын хүчтэй хэрэгсэл болох Mathlab хэлний функцуудыг ашиглан дүрс сегментчилж гарсан үр дүнгийн харьцуулалтыг танилцууллаа. Туршилтын үр дүнд RGB өнгөний орон зайн B бүрэлдэхүүнд суурилсан Otsu-ийн аргийг ашиглан дүрсийг сементчилэх нь уламжлалт дүрс сегментчилэх аргаас нэн сайн үр дүнтай илүү нарийвчлалтай байна. Үүнийг цаашдын судалгааны ажилдаа хэрэглэх болно. Түлхүүр үг: RGB дүрс, босго (Threshold) утга, гистограм, Otsu-ийн арга, дүрс боловсруулалт

Image Segmentation Using Rough Set Theory

2014 ◽  
Vol 1 (2) ◽  
pp. 62-74 ◽  
Author(s):  
Payel Roy ◽  
Srijan Goswami ◽  
Sayan Chakraborty ◽  
Ahmad Taher Azar ◽  
Nilanjan Dey
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Gray Level ◽  
Rule Generation ◽  
Local Minima ◽  
Fuzzy Correlation ◽  
Segmentation Methods

In the domain of image processing, image segmentation has become one of the key application that is involved in most of the image based operations. Image segmentation refers to the process of breaking or partitioning any image. Although, like several image processing operations, image segmentation also faces some problems and issues when segmenting process becomes much more complicated. Previously lot of work has proved that Rough-set theory can be a useful method to overcome such complications during image segmentation. The Rough-set theory helps in very fast convergence and in avoiding local minima problem, thereby enhancing the performance of the EM, better result can be achieved. During rough-set-theoretic rule generation, each band is individualized by using the fuzzy-correlation-based gray-level thresholding. Therefore, use of Rough-set in image segmentation can be very useful. In this paper, a summary of all previous Rough-set based image segmentation methods are described in detail and also categorized accordingly. Rough-set based image segmentation provides a stable and better framework for image segmentation.

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.

scholarly journals Selected Applications of Scale Spaces in Microscopic Image Analysis

2015 ◽  
Vol 15 (7) ◽  
pp. 5-12
Author(s):  
Dimiter Prodanov ◽  
Tomasz Konopczynski ◽  
Maciej Trojnar
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Edge Detection ◽  
Scale Space ◽  
Microscopic Image ◽  
Space Theory ◽  
Systematic Treatment ◽  
Microscopic Image Analysis ◽  
Segmentation Methods ◽  
Scale Spaces

Abstract Image segmentation methods can be classified broadly into two classes: intensity-based and geometry-based. Edge detection is the base of many geometry-based segmentation approaches. Scale space theory represents a systematic treatment of the issues of spatially uncorrelated noise with its main application being the detection of edges, using multiple resolution scales, which can be used for subsequent segmentation, classification or encoding. The present paper will give an overview of some recent applications of scale spaces into problems of microscopic image analysis. Particular overviews will be given to Gaussian and alpha-scale spaces. Some applications in the analysis of biomedical images will be presented. The implementation of filters will be demonstrated.

Research on Threshold Segmentation Algorithms

2013 ◽  
Vol 860-863 ◽  
pp. 2888-2891
Author(s):  
Yu Bing Dong ◽  
Ming Jing Li ◽  
Ying Sun
Keyword(s):  
Pattern Recognition ◽  
Image Segmentation ◽  
Segmentation Method ◽  
Threshold Segmentation ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Better Than ◽  
Iterative Threshold

Thresholding is one of the critical steps in pattern recognition and has a significant effect on the upcoming steps of image application, the important objectives of thresholding are as follows, and separating objects from background, decreasing the capacity of data consequently increases speed. Various threshold segmentation methods are studied. These methods are compared by using MATLAB7.0. The qualities of image segmentation are elaborated. The results show that iterative threshold segmentation method is better than others.

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.

A Comparative Study of Image Segmentation Based on the Improved Meanshift Software with Edison

2014 ◽  
Vol 701-702 ◽  
pp. 253-256
Author(s):  
Ning Li ◽  
Jing Wen Xu ◽  
Jun Fang Zhao ◽  
Yu Dan Zhao ◽  
Peng Hou
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Pattern Recognition ◽  
Image Segmentation ◽  
Comparative Study ◽  
Human Perception ◽  
Texture Segmentation ◽  
Contrastive Analysis ◽  
Different Characteristics

Image segmentation is the technique and the process to separate the image into regions which have different characteristics and extract the interested objects from the image. Meanwhile, image segmentation is a vital important issue in many fields such as image processing, pattern recognition and artificial intelligence and it has wide application in various fields. This paper performs a great deal of contrastive analysis experiments on a series of images by using improved meanshift software and Edison software. The results show that improved meanshift software is easier to segment clearly than Edison in terms of similar color; the improved meanshift software segmentation is smoother than Edison in image shadow, the segmentation results hold favorable consistency in terms of human perception; the improved meanshift software segmentation is clearer than Edison in texture segmentation such as vegetation. The improved meanshift software has a better effect on the segmentation of boundary, road, etc. Both of them can remove the noise points effectively, but improved meanshift software is more sensitive to brightness; while the Edison software has a faster speed compared to the improved meanshift software.

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.

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