基于统计信息的城市道路图像分割算法

De-Shun Yang, Jianhou Gan, Yi Luo
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引用次数: 2

摘要

城市道路图像分割是智能城市管理和无人驾驶的重要技术。为了提高图像分割效果,针对传统区域生长算法种子点单一、阈值固定的不足,提出了一种基于二维直方图灰度和局部方差的种子选择方法,并利用动态阈值改变区域生长规律。实验结果表明,该方法选择的种子具有较高的代表性,实现了对图像的完整分割。基于动态阈值区域增长规则的图像分割效果较好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Urban Road Image Segmentation Algorithm Based on Statistical Information
Urban road image segmentation is an important technology of intelligent city management and pilotless driving. In order to improve the effect of image segmentation, direct at the deficiency of single seed point and fixed threshold of traditional region growing algorithm, a seed selection method based on the gray level of two-dimensional histogram and local variance is proposed, and the dynamic threshold is used to change the region growing rule. The experimental results show that the seeds selected by this method can be highly representative, and realize the complete segmentation of the image. Based on the dynamic threshold region growth rule, the image segmentation has a better effect.
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