一种改进的高浓度风沙图像的动态灰度阈值分割算法

梅凡民; 雒遂; 陈金广

doi:10.6052/0459-1879-18-040

一种改进的高浓度风沙图像的动态灰度阈值分割算法

doi: 10.6052/0459-1879-18-040

梅凡民^1,*, ,,
雒遂¹,
陈金广²

1 西安工程大学环境与化学工程学院, 西安 710048
2 西安工程大学计算机科学学院, 西安 710048

基金项目: 国家自然科学基金 (41340043)、陕西省科技厅自然科学基金 (2014JM5207) 和陕西省教育厅专项 (14JK1291) 资助项目.

详细信息

作者简介:
通讯作者:梅凡民,教授,主要研究方向：风沙多相流力学与大气环境. E-mail:meifanmin@xpu.edu.cn

通讯作者:
梅凡民

中图分类号: TP751.1;
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出版历程
- 收稿日期: 2018-02-11
- 刊出日期: 2018-05-18

AN IMPROVED ALGORITHM OF DYNAMIC GRAY-THRESHOLDING FOR SEGMENTING DENSE AEOLIAN SAND PARTICLES IMAGES

Mei Fanmin^{1,*
, ,},
Luo Sui¹,
Chen Jinguang²

1 School of Environmental and Chemical Engineering, Xi’an Polytechnic University, Xi’an 710048, China
2 School of Computer Science, Xi’an Polytechnic University, Xi’an 710048, China

摘要

摘要: 风沙跃移是塑造干旱区地貌的最主要的动力,其伴生的粉尘的释放、输送和沉降不但严重地影响了大气环境质量,也引起了全球气候系统和海洋系统的变化. 单个沙粒轨迹形成是理解粒--床碰撞过程和风沙两相流耦合过程的重要纽带. 高浓度风沙图像处理算法是深入理解沙粒轨迹形成机制的关键技术. 为了实现对高浓度风沙图像精细处理, 本文提出了不依赖于经验参数的动态灰度阈值分割算法,它包括背景模板去噪、绿光通道灰度化处理、图像微分、灰度方差阈值目标检测和最大类间方差灰度阈值分割等,其中背景模板去噪和灰度方差阈值目标检测等是新算法的主要亮点. 高浓度风沙图像分割实验显示, 扣减背景模板去除了条纹状和斑点状的稳定噪声;图像微分和灰度方差阈值目标检测显著地提高了暗沙粒识别的数量并有效地去除了随机噪声; 改进算法的沙粒有效识别个数(人机判读坐标一致的沙粒数目)、查全率(计算机提取沙粒数目与人工判读的实际沙粒数比值)和查准率(有效沙粒数目与实际沙粒数目的比值) 分别为461, 71%和86%,显著地高于传统算法对应的85, 13%和82%,这表明新算法对高浓度风沙图像的分割效果良好. 避免单粒子分割和表观重叠问题是进一步完善高浓度风沙图像的分割算法的可能途径.
- 风沙 /
- 图像分割 /
- 动态灰度阈值 /
- 类间方差 /
- 查全率 /
- 查准率
Abstract: Aeolian saltation plays a very key role in shaping varieties of landforms in arid area and in affecting global climate system and marine ecosystem. It is generalized as four sub-processes: aerodynamic entrainment, the grain trajectory, the grain-bed collision and wind modification. Among these sub-processes, individual particle trajectory formation is a key chain affecting grain-bed collision and interaction between sand particles and wind conditions. Nevertheless, up to date, the mechanism of sand particle trajectory formation has not yet disclosed perfectly due to lack of a sophisticated algorithm for extracting effectively sand particles from high-concentration images. The traditional algorithms with single thresholding often cannot segment sand particles effectively from backgrounds due to brightness difference among saltating particles, stable and stochastic noises in high-concentration images. The algorithm with dynamic thresholding proposed by Ohmi and Li, however, needs to preset arbitrarily empirical parameters as maximum, minimum and contrast threshold, probably introducing uncertainty of segmentation. Thus, an improved segmentation- algorithm with dynamic thresholding is proposed here, which covers denosing by substracting a background image, graying by green channel, differentiation, targets’ detection by gray-level variance and segmenting by maximum between-class variance of gray-thresholding. The highlights of new algorithm lie in two aspects: the denosing by substacting a background image and the targets’ detection. In virtue of the denosing method, such stable noise signs as stripes and maculae deriving from photography processes are removed effectively from the sand particles’ image. As the most an important precedure of the improved algorithm, the targets’ detection is able to distinguish effectively those dark sand particles from background in differential units by selection of appropriate variance of gray (3.5), but also to reduce fasle information that backgrounds could be recognized wrongly as particles. It seems that image segmented with the targets’ detection shows clearly more sand particles in contrast to image without the targets’ detection which is blurred with lots of stochastic noises. Based on horizontal and vertical coordinates of all sand particle in the study image recorded manually, such parameters as the number of sand particles identified correctly (N_ie), recall rate (R_c) (refers to ratio of the extracted automatically number of sand particles to the number of real particles (N_r)) and the precision (P_r refers to ratio of N_ie to N_r) were used to evaluate the algorithm. It shows that N_ie, R_c and P_r is 461, 71%和86% respectively, compared to 85, 13% and 82% by the traditional algorithm. The new algorithm is better than the traditional one. Nevertheless, it should be perfected in future through many new ways.
- aeolian sand particles /
- image segmentation /
- dynamic grey-thresholding /
- between-class variance /
- recall rate /
- precision

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参考文献(1)

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