  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.
