DocumentCode :
2206354
Title :
An improved moment-preserving auto threshold image segmentation algorithm
Author :
Luo, Shitu ; Zhang, Qi ; Luo, Feilu ; Wang, Yanling ; Chen, Zhiyong
Author_Institution :
Coll. of Mechatronics Eng. & Autom., Nat. Univ. of Defense Technol., Changsha, China
fYear :
2004
fDate :
21-25 June 2004
Firstpage :
316
Lastpage :
318
Abstract :
When moment-preserving auto threshold algorithm is used to segment image whose histogram is unimodal or monotonic function, there is serious background interference, and the segmentation accuracy is greatly affected by the size variance of the object, so this paper puts forward an improved moment-preserving auto threshold algorithm. Aiming at the original algorithm´s shortage of neglecting image details, this algorithm takes advantage of the feature that the grey level difference between object borders and adjacent background is great while the difference among pixels in an object region or background region is small, and then adds gradient adjustment based on object edge pixels to moment-preserving auto thresholding, in order to look after both the whole and the details of image in segmentation result. This algorithm needs no iteration or search, and it is fast enough to satisfy the demand for real time. As simulation results show, this algorithm can segment object image effectively.
Keywords :
estimation theory; image segmentation; method of moments; random functions; background interference; gradient adjustment; image segmentation algorithm; moment-preserving auto threshold algorithm; monotonic function histogram; object edge pixels; unimodal function histogram; Automation; Educational institutions; Histograms; Image edge detection; Image segmentation; Interference; Mechatronics; Moment methods; Parameter estimation; Pixel;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Acquisition, 2004. Proceedings. International Conference on
Print_ISBN :
0-7803-8629-9
Type :
conf
DOI :
10.1109/ICIA.2004.1373378
Filename :
1373378
Link To Document :
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