Image Segmentation of Multi-Shaped Overlapping Objects
This work addresses a domain-specific challenge in image analysis for materials science, offering an incremental improvement over existing methods.
The authors tackled the problem of segmenting overlapping convex objects of multiple shapes in images, proposing a two-step algorithm that first segments visible contours and then assigns shape identities, and demonstrated its effectiveness by outperforming two baselines on crystal image datasets.
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.