Automatic Segmentation of Pores in Weld Images Based on Transition Region Extraction

2012 ◽  
Vol 217-219 ◽  
pp. 1964-1967
Author(s):  
Tong Tong ◽  
Yan Cai ◽  
Da Wei Sun ◽  
Peng Liu
Keyword(s):  
Transition Region ◽  
Automatic Segmentation ◽  
Experimental Results ◽  
Gray Level ◽  
Segmentation Method ◽  
Complex Environment ◽  
Weld Defects ◽  
Contrast Enhanced ◽  
Region Extraction ◽  
Low Pass

In allusion to the complex images of weld defects, weak contrast between the target and the background, a new segmentation method based on gray level difference transition region extraction is proposed. The paper analyzes the characteristic of weld defects, and then low-pass filtering and contrast enhanced are used to enhance the clarity. Finally, we extract the transition region and confirm a threshold for defects segmentation. The experimental results show that the method can extract the transition region more accurate, and segment the image much better in complex environment.

The Combination of Local Fuzzy Entropy and Gray Level Difference-Based Thresholding Segmentation

2012 ◽  
Vol 220-223 ◽  
pp. 1288-1291
Author(s):  
Tong Tong ◽  
Yan Cai ◽  
Da Wei Sun ◽  
Wei Huang
Keyword(s):  
Transition Region ◽  
Extraction Methods ◽  
Fuzzy Entropy ◽  
Experimental Results ◽  
Segmentation Result ◽  
Gray Level ◽  
Thresholding Method ◽  
Level Difference ◽  
Region Extraction

A novel transition region extraction and thresholding method based on both frequency and degree of gray level changes is proposed by analyzing properties of transition region. Frequent gray level based transition region extraction methods are greatly affected by noise. To eliminate the algorithm limitation, a modified descriptor taking both degree and frequency of gray level changes into account is developed. The proposed algorithm can accurately extract transition region of an image and get ideal segmentation result. The experimental results show its superiority and feasibility.

Underwater Image Transition Region Extraction and Segmentation Based on SVM

2013 ◽  
Vol 397-400 ◽  
pp. 2171-2176 ◽  
Author(s):  
Cong Ping Chen ◽  
Lei Zou ◽  
Wei Wang
Keyword(s):  
Support Vector Machine ◽  
Transition Region ◽  
Extraction Method ◽  
Support Vector ◽  
Gray Level ◽  
Local Difference ◽  
Underwater Image ◽  
Local Complexity ◽  
Region Extraction ◽  
Segmentation Threshold

By analyzing the gray level features of transition region, a new underwater image transition region extraction method based on Support Vector Machine (SVM) is presented. At first, a vector is constructed to fully describe the transition region, which includes local complexity, local difference and neighborhood homogeneity. Then, SVM is applied to train and classify the set of feature vectors, so that the transition region of the underwater image is extracted. Finally, the segmentation threshold is determined by mean of the histogram of the transition region, and the binary result was yielded. The experimental results show that the proposed algorithm can achieve a better transition region extraction and segmentation performance, and automatically select the optimal threshold for transition region extraction.

The Segmentation Method Based on Fusion Algorithm of Transition Region Extraction for Coronary Angiograms

2012 ◽  
Vol 542-543 ◽  
pp. 616-619 ◽  
Author(s):  
Wen Wei Kang ◽  
Xiao Tao Kang ◽  
Bin Liu
Keyword(s):  
Transition Region ◽  
Coronary Arteries ◽  
Segmentation Method ◽  
Fusion Algorithm ◽  
Small Vessels ◽  
Background Elimination ◽  
Local Complexity ◽  
Region Extraction ◽  
Segmentation Image ◽  
Coronary Angiograms

Aiming at the complex background of coronary angiograms, weak contrast between the coronary arteries and the background, a new segmentation method based on transition region extraction is proposed. Firstly, the coronary arteries are extracted by using the local complexity method based on Top-hat. Then the coronary arteries are extracted again by using the local complexity method based on Gaussian filter. Finally, the segmentation image is obtained by fusing two extracted coronary arteries images. The experiments indicate that the proposed method has better performance on the small vessels extraction and background elimination. In addition, the method is valuable for diagnosis and the quantitative analysis of vessels.

scholarly journals Gray Level Image Threshold Using Neutrosophic Shannon Entropy

2019 ◽  
Author(s):  
Vasile Patrascu
Keyword(s):  
Shannon Entropy ◽  
Experimental Results ◽  
New Method ◽  
Gray Level ◽  
Segmentation Method ◽  
Separation Threshold ◽  
Multiple Thresholds ◽  
Gray Level Image ◽  
Information Components ◽  
Grayscale Images

This article presents a new method of segmenting grayscale images by minimizing Shannon's neutrosophic entropy. For the proposed segmentation method, the neutrosophic information components, i.e., the degree of truth, the degree of neutrality and the degree of falsity are defined taking into account the belonging to the segmented regions and at the same time to the separation threshold area. The principle of the method is simple and easy to understand and can lead to multiple thresholds. The efficacy of the method is illustrated using some test gray level images. The experimental results show that the proposed method has good performance for segmentation with optimal gray level thresholds.

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

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