This paper is focused on the study and implementation of auto-detecting of distresses in pavement image.
本文致力于路面图象病害自动检测算法的研究和实现。
When acquiring the pavement image, the acquired image is hard for image segmentation and recognition because of the noise and the nonuniform background illumination.
在获取路面图像中，由于背景光照不均匀以及存在着一些路面噪声，给后来的图像分割及识别带来了一些困难。
It is shown that the improved DBC approach can convert pavement image to one whose fractal dimension permits simple thresholding for segmentation of pavement cracks.
结果显示差分计盒方法可以将路面图像转换成另一种图像，该图像的分形维数可以把简单的阈值应用到路面裂缝的分割。
The paper studies mean filter, SUSAN filter, median filter and gauss filter technology, and selects the fast median filter to get good smoothing result of pavement image.
研究了均值滤波、SUSAN滤波、高斯滤波和中值滤波技术，对缺陷图像进行了平滑去噪处理，最后选用快速中值滤波并取得了良好的效果，为下一步图像分割打好了基础。
A method of asphalt pavement surface distress image segmentation is put forward, and it may reduce calculation of pavement surface distress image classify.
提出了一种减少沥青路面破损图像识别计算量的图像分割方法。
A method of asphalt pavement surface distress image segmentation is put forward, and it may reduce calculation of pavement surface distress image classify.
提出了一种减少沥青路面破损图像识别计算量的图像分割方法。
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