基于图像内容的沥青路面病害区域分割算法
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摘要
针对CCD采集的沥青路面病害图像分辨率过高且含信息的有效区域占比少的问题,提出一种基于图像内容的沥青路面病害图像区域分割算法,以剔除路面图像中的无效区域.首先通过预处理和病害提取过程将原图像处理成包含病害特征的二值图像;然后通过计算整幅图像中含信息像素的上下比和左右比,得到初始化遍历方向,并统计各行(或列)的含信息像素总数;最后从初始化遍历方向开始遍历并丢弃含信息量最少的行(或列),最终得到分割后的图像.为验证算法的有效性与合理性,采用图像信息熵作为算法评价标准与传统算法进行对比分析.实验结果表明:本文算法在有效降低图像分辨率的前提下能很好的保留病害目标信息,提升图像信息熵.
The resolution of the asphalt pavement disease image which collected by the CCD is too high and the area of the effective area containing information is small. A region segmentation algorithm for asphalt pavement disease image based on image content was proposed to eliminate the invalid region in the pavement image. Firstly, the original image was processed into a binary image containing the disease characteristics through a process of preprocessing and disease extraction. Then the initial traversal direction is obtained by calculating the up-to-down ratio and the left-to-right ratio of the pixels which contain information in the whole image, and counting the total number of information pixel of each row(or column). Finally, traversing from the initial traversal direction and discarding the row(or column) with the least amount of information in order to finally obtain the segmented image. In order to verify the validity and rationality of the algorithm, the image information entropy was used as the algorithm evaluation standard and compared with the traditional algorithm. The experimental results show that the proposed algorithm can keep the target information very well on the premise of effectively reducing the image resolution, and improve image information entropy.
The resolution of the asphalt pavement disease image which collected by the CCD is too high and the area of the effective area containing information is small. A region segmentation algorithm for asphalt pavement disease image based on image content was proposed to eliminate the invalid region in the pavement image. Firstly, the original image was processed into a binary image containing the disease characteristics through a process of preprocessing and disease extraction. Then the initial traversal direction is obtained by calculating the up-to-down ratio and the left-to-right ratio of the pixels which contain information in the whole image, and counting the total number of information pixel of each row(or column). Finally, traversing from the initial traversal direction and discarding the row(or column) with the least amount of information in order to finally obtain the segmented image. In order to verify the validity and rationality of the algorithm, the image information entropy was used as the algorithm evaluation standard and compared with the traditional algorithm. The experimental results show that the proposed algorithm can keep the target information very well on the premise of effectively reducing the image resolution, and improve image information entropy.
引文
1 Sun L,Kamaliardakani M,Zhang YM.Weighted neighborhood pixels segmentation method for automated detection of cracks on pavement surface images.Journal of Computing in Civil Engineering,2016,30(2):04015021[doi:10.1061/(ASCE)CP.1943-5487.0000488]
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12文立.改进的灰度校正算法在路面裂缝图像预处理中应用.计算机系统应用,2015,24(2):220-223.[doi:10.3969/j issn.1003-3254.2015.02.041]
13 Yang W,Xiao ZT,Wang QJ,et al.A method for improving the definition of scene in fog image.2009 Internationa Conference on Computational Intelligence and Softwar Engineering.Wuhan,China.2009.1-4.
2高健,茅时群,周宇玫,等.一种基于映射图像子块的图像缩小加权平均算法.中国图象图形学报,2006,11(10):1460-1463.[doi:10.3969/j.issn.1006-8961.2006.10.018]
3常军,吴锡生.基于图像子块加权缩小的自适应修正算法计算机工程与应用,2009,45(7):181-183,186.[doi:10.3778/j issn.1002-8331.2009.07.054]
4冯杰飞,韩慧健.基于非均匀B样条插值算法的图像放大计算机应用,2010,30(1):82-84,89.
5 Mishiba K,Ikehara M,Yoshitome T.Content aware image resizing with constraint of object aspect ratio preservation IEICE Transactions on Information and Systems,2013,E96-D(11):2427-2436.[doi:10.1587/transinf.E96.D.2427]
6 Niu YZ,Liu F,Li XQ,et al.Image resizing via nonhomogeneous warping.Multimedia Tools and Applications2012,56(3):485-508.[doi:10.1007/s11042-010-0613-0]
7 Avidan S,Shamir A.Seam carving for content-aware image resizing.ACM Transactions on Graphics,2007,26(3):10[doi:10.1145/1276377]
8 Choi J,Kim C.Sparse seam-carving for structure preserving image retargeting.Luwer Academic Publishe,2016,85(2):275-283.
9邹盼盼,陆平,朱恒亮,等.基于主体区域保持的图像缩放算法.图学学报,2016,37(2):230-236.
10林晓,沈洋,马利庄,等.显著物体形状结构保持的图像缩放方法.计算机科学,2014,41(12):288-292.[doi:10.11896/j issn.1002-137X.2014.12.062]
11宋蓓蓓,韦娜.FCM分割和形态学的沥青路面图像裂缝提取.计算机工程与应用,2013,49(4):31-34.[doi:10.3778/j issn.1002-8331.1208-0201]
12文立.改进的灰度校正算法在路面裂缝图像预处理中应用.计算机系统应用,2015,24(2):220-223.[doi:10.3969/j issn.1003-3254.2015.02.041]
13 Yang W,Xiao ZT,Wang QJ,et al.A method for improving the definition of scene in fog image.2009 Internationa Conference on Computational Intelligence and Softwar Engineering.Wuhan,China.2009.1-4.