Recognition of highly overlapping ellipse-like bubble images
Recognition of highly overlapping ellipse-like bubble images
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DOI：
10.1088/0957-0233/16/9/007
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发表时间：
2005-09-01
影响因子：
2.4
通讯作者：
Niinimäki, J
中科院分区：
文献类型：
--
作者：
Honkanen, M;Saarenrinne, P;Niinimäki, J
这项研究描述了一种稳健的气泡图像识别算法，该算法从具有严重重叠的气泡的实验图像中检测出聚焦的椭圆形气泡图像。重叠对象识别(OOR)算法的原理是计算线段的整体周长，找出周长上代表重叠对象连接点的点，对属于同一对象的周长圆弧进行聚类，并在圆周圆弧上拟合椭圆。用重叠椭圆的模拟图像研究了该算法的精度，在尺寸测量中提供了0.9个像素的均方根误差。该算法被用于测量气泡尺寸分布的直接成像(DI)技术，其中使用数码相机和脉冲背光来检测气泡轮廓。利用凝胶中的停滞气泡来校准测量系统，以便定义与气泡大小相关的测量体积的有效厚度和作为聚焦标准的灰阶梯度阈值。所描述的概念和新的气泡识别算法使密度更大的泡状流中的DI测量具有更高的可靠性和测量结果的准确性。将该测量技术应用于造纸机离心泵出口管内湍流泡状流的研究。
This study describes a robust bubble image recognition algorithm that detects the in-focus, ellipse-like bubble images from experimental images with heavily overlapping bubbles. The principle of the overlapping object recognition (OOR) algorithm is that it calculates the overall perimeter of a segment, finds the points at the perimeter that represent the connecting points of overlapping objects, clusters the perimeter arcs that belong to the same object and fits ellipses on the clustered arcs of the perimeter. The accuracy of the algorithm is studied with simulated images of overlapping ellipses, providing an RMS error of 0.9 pixels in size measurement. The algorithm is utilized in measurements of bubble size distributions with a direct imaging (DI) technique in which a digital camera and a pulsed back light are used to detect bubble outlines. The measurement system is calibrated with stagnant bubbles in a gel in order to define the bubble size dependent effective thickness of the measurement volume and the grey scale gradient threshold as a focus criterion. The described concept with a novel bubble recognition algorithm enables DI measurements in denser bubbly flows with increased reliability and accuracy of the measurement results. The measurement technique is applied to the study of the turbulent bubbly flow in a papermaking machine, in the outlet pipe of a centrifugal pump.