A robust and easy-to-apply automatic static mask generation technique for PIV images is described. The technique is used after background removal operations and requires an image ensemble with sufficient number of single-frame or double-frame PIV images. The technique does not require any prior knowledge of the flow, illumination conditions, the bounding surface, or static object geometry. The technique categorically marks the fluid phase by exploiting the simple fact that PIV seeding particles reside in the flow medium. There may be a combination of stationary and moving objects in the ensemble, and the technique is not affected by the presence of moving objects, i.e. dynamic masking techniques can still be applied after the static masking operation described here. The generation of static masks of the fluid phase is important as these masks are used to improve accuracy and spatial resolution close to the walls during the evaluation steps of the advanced PIV algorithms. In order to demonstrate the efficiency and limitations of the technique we use several examples including the microfluidics case (Case A) and the flow over periodic hills case (Case B) in the 4th international PIV challenge.

描述了一种强大且易于应用的PIV图像自动静态蒙版生成技术。该技术在背景去除操作之后使用，并且需要具有足够数量的单帧或双帧PIV图像的图像集合。该技术不需要任何有关流，照明条件，边界表面或静态对象几何形状的先验知识。该技术通过利用PIV播种粒子驻留在流动介质中这一简单事实来分类标记液相。集合中可能存在固定对象和移动对象的组合，并且该技术不受移动对象的存在的影响，即，在此处描述的静态遮罩操作之后，仍可以应用动态遮罩技术。流体相的静态蒙版的生成很重要，因为在高级PIV算法的评估步骤中，这些蒙版用于提高靠近壁的精度和空间分辨率。为了证明该技术的效率和局限性，我们使用了几个例子，包括第四届国际PIV挑战赛中的微流体案例（案例A）和周期性丘陵流量（案例B）。