| 高速影像中风沙颗粒灰度和表观粒径的变化特征及对多假设追踪算法的意义 |
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| 引用本文： | 梅凡民,张梦莲.高速影像中风沙颗粒灰度和表观粒径的变化特征及对多假设追踪算法的意义[J].中国沙漠,2019,39(3):7-16. |
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| 作者姓名： | 梅凡民 张梦莲 |
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| 作者单位： | 西安工程大学 环境与化学工程学院, 陕西 西安 710048 |
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| 基金项目： | 国家自然科学基金项目（41340043）；陕西省科技厅项目（2014JM5207）；陕西省教育厅项目（14JK1291） |
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| 摘 要： | 沙粒轨迹形成过程是理解风沙两相流输运力学机制的关键问题。多假设追踪算法通过融合追踪目标的多个信息特征和建立后验概率控制的动态递归算法来实现对目标的准确追踪,其精度可能优于传统的基于位置信息的轨迹追踪算法。基于多假设追踪算法的需要,以沙粒灰度和表观粒径(沙粒在影像上显现的大小)为对象,通过分析沙粒在轨迹形成过程中灰度和表观粒径的辨识度及稳定性来揭示它们作为多假设轨迹算法的特征参数的可能性和可靠性。结果表明:沙粒灰阶25~255,具有多峰分布的特征,表明不同沙粒灰度具有很好的区分度;80%~85%的沙粒在入射、反弹过程及粒-床碰撞后,灰度差在1倍标准偏差之内,表明大多数沙粒的灰度具有相对的稳定性;沙粒的表观粒径分布于77~1 001μm,表明表观粒径具有较好的区分度;97%~100%的沙粒在入射、反弹过程中、77%的沙粒在粒-床碰撞后,表观粒径差均是稳定的(77~154μm);仅有15%~23%的沙粒发生了显著的侧向运动或自旋,表明多数沙粒的灰度和表观粒径的稳定性不受侧向运动或自旋的影响;灰度和表观粒径可以作为多假设追踪算法的重要辨识参数。
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| 关 键 词： | 高速影像 风沙颗粒 灰度 表观粒径 多假设追踪算法 |
| 收稿时间： | 2018-05-19 |
| 修稿时间： | 2018-09-27 |
Analyzing Variation in Gray Level and Apparent Grain Size of Aeolian Saltating Particles from High-speed Images in Terms of Multiple Hypothesis Tracking |
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| Mei Fanmin,Zhang Menglian.Analyzing Variation in Gray Level and Apparent Grain Size of Aeolian Saltating Particles from High-speed Images in Terms of Multiple Hypothesis Tracking[J].Journal of Desert Research,2019,39(3):7-16. |
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| Authors: | Mei Fanmin Zhang Menglian |
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| Institution: | School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an 710048, China |
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| Abstract: | As a key issue that many scientists have taken efforts to sovle since 1980's, the non-correspondence between dynamic paremeters of turblent wind and saltation flux at high time resolution is not been understood perfectly. A high-precision algorithm to be developed for tracking massive sand particles from 2D high-speed images is very fundamental to understand this issue in terms of individual saltating particle trajectory formation driven by turbluent wind. It can enhance precision of multiple hypothesis tracking by introduction of new parameters as gray level and apparent grain size of saltating particles if they are discriminative and stable well in the 2D images. However, discrimination and stability of gray level and apparent grain size have not been discussed as so far. As a result, this paper aims to discuss this topic based on 1 000 frames of high-speed images. Gray level of saltating particles varies around 25-255, showing a nice discrimination. Gray level difference of about 80%-85% saltating particles is below one times standard deviation as they impact and rebound, which indicates the parameter sound stable. Range around 77-1 001 μm shows apparent grain size can discriminate one well from anothor particle, while apparent grain size difference of 97%-100% incidence or rebound particles and that of 77% incidence and rebound particles is stable. Only about estimated 15%-23% of saltating particles moves transversely or rotationally based on variations in gray level and apparent grain size, not affecting stability of gray level and apparent grain size of majority of saltating particles. Thus, gray level and apparent grain size can be used as characteristic parameters besides position for the multiple hypothesis tracking. |
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| Keywords: | high-speed images aeolian saltation gray level apparent grain size multiple hypothesis tracking |
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