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Application Research of Active Contour Model in Image Segmentation

Author: YaoBin
Tutor: CaoBoYan
School: Xi'an University of Electronic Science and Technology
Course: Computer System Architecture
Keywords: Image Segmentation Dynamic contour model GVF Critical point Bottle-type objects
CLC: TP391.41
Type: Master's thesis
Year: 2008
Downloads: 112
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Abstract


Image understanding and active vision , image segmentation is one of the most basic problems . As a dynamic contour model image segmentation algorithm guided by high-level knowledge , is a hot research topic in recent years, the field of image understanding . This paper first introduces the commonly used method of image segmentation and classification , and a detailed analysis of the parameterized dynamic contour model and two categories of dynamic contour model dynamic contour model based on the geometric characteristics , and their respective advantages and disadvantages , the last for which GVF Snake model and its improved model GGVF Snake model can not extract some depth complete contour defects sunken objects and bottle -type objects , a new segmentation contour detection algorithm . The new algorithm is based on the depth of the Depression object of objects and bottles GVF ( including GGVF) external force field critical point and the stagnation point region the segmented detection entire outline of the object . False contour is automatically set an initial contour that first set to get the depth of depression the objects and bottle -type objects to non-depressed part of the outline , and then formed according to the stagnation point region contains of objects external critical point initial contour to get objects depression part of the outline , the final integration of the two-part profile to get a complete outline of the depth of depression objects and bottle -type objects .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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