Recognition of highly overlapping ellipse-like bubble images

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.