科研之友
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摘要
A new transformation for finding global valleys in 1D distributions, with particular application to the thresholding of grey-scale images is described. The applied criterion function estimates the global signifiance of all the valleys that are located, and thus can cope with multi-mode distributions. Examples make it clear that one of the main advantages of the resulting global valley method (GVM) is that it permits partially hidden minima to be reliably located without complicated analysis. Overall, the GVM is demonstrated to have very good stability properties and high sensitivity for the detection of subsidiary minima - including those arising near the ends of distributions that arise from practically important image detail ( such as defects or contaminants in an automated inspection scenario). The global analysis around which the method is formulated is responsible for achieving these capabilities with modest computational demands.
- 出版日期2008-6
