风沙颗粒运动的数字高速摄影图像的分割算法

为了深入理解多因素驱动下风沙颗粒起动的动态演化规律, 需要准确地获得沙质床面附近沙粒群起跳的方式、速度和运动轨迹. 以连续强激光源为照明的数字高速摄影技术是研究这类问题的有效手段, 但由于风沙运动的高速摄影图像具有运动沙粒和静止床面的对比度小、相邻两帧图像相似性小等特点, 原始图像叠加算法难以有效实现目标与背景的分割. 该文提出了基于相邻的风沙运动图像灰度差值变化原理的图像分割算法. 实例显示,只要选择合适的相邻图像灰度差值阈值和自适应二值化处理方法就能实现图像中运动沙粒与床面分割. 当起跳沙粒浓度较低情况下, 基于MATLAB平台的最小距离匹配的粒子追踪算法(PTV算法)能较为准确地恢复床面附近沙粒的运动轨迹. 

An arithmetic method of segmenting moving aeolian sand particles' images from background information of digital high-speed photography images

To deeply understand dynamic evolutional mechanism of initial of aeolian sand particles by multiple factors,it is very critical to measure precisely liftoff velocities and trajectories of sand particles with different movement types.Although the high-speed photography illuminated by intensive and continuous laser is an effective method for obtaining the above information,the kinds of high- speed photography images cannot be processed precisely by overlapping image method due to very low contrast ratio of objectives to backgrounds and significant discrepancy between two frame images.As a result,a new arithmetic method of image segmentation is proposed on basis of a principle of variation in gray level difference between two frame consecutive images. The cases show that the new arithmetic method can segment successfully saltating particles from immobile bed by selecting appropriate threshold value of gray level difference between two continuous images and by adopting adaptive binary method.The new arithmetic method plus a minimum distance matching method of particles tracking velocimetry(PTV) can retrieve precisely sand particles trajectories near the bed as sand particles concentration is low.