An improved moment-preserving auto threshold image segmentation algorithm

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-pr...

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Bibliographic Details
Published inInternational Conference on Information Acquisition, 2004. Proceedings pp. 316 - 318
Main Authors Shitu Luo, Qi Zhang, Feilu Luo, Yanling Wang, Zhiyong Chen
Format Conference Proceeding
LanguageEnglish
Published IEEE 2004
Subjects
Automation
Educational institutions
Histograms
Image edge detection
Image segmentation
Interference
Mechatronics
Moment methods
Parameter estimation
Pixel
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Summary: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.
ISBN:9780780386297
0780386299
DOI:10.1109/ICIA.2004.1373378