In this work, we propose a new segmentation algorithm for images containing convex objects present in multiple shapes with a high degree of overlap. The proposed algorithm is carried out in two steps, first we identify the visible contours, segment them using concave points and finally group the segments belonging to the same object. The next step is to assign a shape identity to these grouped contour segments. For images containing objects in multiple shapes we begin first by identifying shape classes of the contours followed by assigning a shape entity to these classes. We provide a comprehensive experimentation of our algorithm on two crystal image datasets. One dataset comprises of images containing objects in multiple shapes overlapping each other and the other dataset contains standard images with objects present in a single shape. We test our algorithm against two baselines, with our proposed algorithm outperforming both the baselines.
We address the problem of segmenting multiple similar objects by optimizing a Chan-Vese-like [1] functional with respect to a mixture of level set functions. We solve the variational formulation under this model allowing for similarity transforms. This allows shape priors to be enforced even in the presence of mutual occlusion, lifting the limitation in [2]. We show numerical results on example images to demonstrate the promise of our approach. Direct imaging technology is an effective and convenient method for the estimation of bubble size distribution (BSD). However, overlapping bubble has an influence on BSD when gas holdup is more than 1 . In this paper, we present a new method of overlapping elliptical bubble recognition to determine bubble size. The method mainly includes two steps: contour segmentation and segment grouping. Contour segmentation is on the assumption that the concave points in the dominant point sequence are always the connecting points, and segment grouping is mainly based on the average distance deviation criterion. Both simulated images and real bubble images are used to evaluate this new method. The results show that it is effective in the recognition of overlapping elliptical bubbles and have a potential in other elliptical object recognition. In the last, two methods are used for BSD estimation. It is found that the bubble size (such d10 or d32) estimated by the ignore method is slightly smaller than that estimated by the recognition method. 