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Testing for Curves in a Binary Image

机译:测试二进制图像中的曲线

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摘要

Curve detection is viewed as a process of hypothesis generation and hypothesis testing. Of the two, hypothesis generation has received much attention and many sophisticated post-processing strategies are published in the literature. In this work, the emphasis is shifted to the development of an efficient and effective hypothesis testing strategy to relieve the hypothesis generation from sophisticated computations. The proposed method recasts edge pixels in a binary image into a one-parameter system derived from the hypothesis. The recasting process creates a histogram, which contains a single dominant peak if and only if the hypothesis under testing contains a significant number of edge pixels. Experiments with circle testing show that the proposed strategy outperforms the global threshold.
机译:曲线检测被视为假设生成和假设检验的过程。在这两种假设中,假设的产生已引起广泛关注,并且许多复杂的后处理策略已在文献中发表。在这项工作中,重点已转移到开发有效的假设检验策略上,以减轻复杂计算中的假设生成。所提出的方法将二值图像中的边缘像素重铸为从该假设得出的一参数系统。重铸过程将创建一个直方图,当且仅当所测试的假设包含大量边缘像素时,该直方图才会包含一个主峰。圆形测试的实验表明,所提出的策略优于全局阈值。

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