DocumentCode :
2601102
Title :
Fast extraction of linear segmentation characteristic based on gray scale projection multiple processing
Author :
Xudong Yang ; Peng Dai ; Ping He ; Qiang Wang ; Hongjian Zhang
Author_Institution :
Dept. of Control Sci. & Eng., Harbin Inst. of Technol., Harbin, China
fYear :
2009
fDate :
5-7 May 2009
Firstpage :
1681
Lastpage :
1684
Abstract :
The fast extraction of segmentation characteristic from images constructed by complex objects and contaminated by noise is a difficult problem in image process domain. A fast extraction algorithm of linear segmentation characteristic based on multiple processing to gray scale projection is proposed. The original gray scale projection of image is mapped with rectangle window to obtain statistics distribution curve. Classical first difference calculator is used to extract the extreme point on the curve. The coordinate of linear segmentation characteristic formed by expected region edges is obtained based on the extreme point gradient criteria. Demonstrated by application, the algorithm is accurate and effective for optical noisy images.
Keywords :
feature extraction; image segmentation; optical images; statistical distributions; classical first difference calculator; fast extraction algorithm; gray scale projection; image construction; image mapping; image process domain; linear segmentation characteristics; optical noisy image; point gradient criteria; statistics distribution curve; Clustering algorithms; Clustering methods; Helium; Image edge detection; Image segmentation; Instrumentation and measurement; Optical noise; Partitioning algorithms; Statistical distributions; Ultraviolet sources; Gray Scale Projection; Linear Segmentation; Mapped with Rectangle Window; Point Gradient;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Instrumentation and Measurement Technology Conference, 2009. I2MTC '09. IEEE
Conference_Location :
Singapore
ISSN :
1091-5281
Print_ISBN :
978-1-4244-3352-0
Type :
conf
DOI :
10.1109/IMTC.2009.5168726
Filename :
5168726
Link To Document :
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