Gray level difference-based transition region extraction and thresholding

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.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.217-219.1964 ◽

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.

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Underwater Image Transition Region Extraction and Segmentation Based on SVM

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.397-400.2171 ◽

2013 ◽

Vol 397-400 ◽

pp. 2171-2176 ◽

Cited By ~ 1

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.

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