The "samefringe" problem of testing trees for equality of tips (even if the trees have a different internal structure) seems to be the main example used to illustrate the usefulness of coroutines in AI languages.
This paper presents a method for the extraction of blood vessels from fundus images. The proposed method is an unsupervised learning method which can automatically segment retinal blood vessels based on an adaptive random sampling algorithm. This algorithm consists in taking an adequate number of random samples in fundus images, and all the samples are contracted to the position of the blood vessels, then the retinal vessels will be revealed. The basic algorithm framework is presented in this paper and several preliminary experiments validate the feasibility and effectiveness of the proposed method.
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