A robust bubble delineation algorithm for froth images

Describes a robust segmentation algorithm for froth images from flotation cells in mineral processing. The size, shape, texture and color of bubbles in a froth image is very important information for optimizing flotation. To determine these parameters, the bubbles in a froth image have to be delineated first. Due to the special characteristics of froth images and a large variation of froth image patterns and quality, it is difficult to use classical segmentation algorithms. Therefore, a new segmentation algorithm was developed to delineate every individual bubble in a froth image. A new segmentation algorithm based on valley-edge detection and edge tracing has been developed. In order to detect bubble edges clearly and disregard the edges of the white spots, the algorithm just detects valley-edges between bubbles in the first step. It detects each image pixel to find if it is the lowest valley point in a certain direction. If it is, the pixel is marked as an edge candidate. Before this procedure, to alleviate noise edges, an image enhancement procedure was added to filter out the noise pixels. After valley-edge detection, the majority of edges are marked at one time, but some small gaps between edges, and noise still exist in the image. To reduce the noise, a clean up procedure was developed. To fill the gaps, an edge tracing algorithm was applied, in which, edges are smoothed into one pixel width. Endpoints and their directions are detected, and edge tracing starts from the detected endpoints. When a new valley-edge pixel is found, the algorithm uses it as a new endpoint, and the valley-edge tracing procedure continues until a contour of a bubble is closed. The segmentation algorithm has been tested on images from Pyhasalmi mine in Finland and Garpenberg mine in Sweden. The processing speed of the algorithm is much faster than for normal morphological segmentation algorithms. The processing accuracy is better than that of manual segmentation result