Image Segmentation of Multi-Shaped Overlapping Objects

TL;DR

This paper introduces a novel two-step image segmentation algorithm designed to accurately identify and classify overlapping convex objects of various shapes, demonstrating superior performance on crystal image datasets.

Contribution

The paper presents a new segmentation method that effectively handles multi-shaped overlapping objects and assigns shape identities, outperforming existing baseline algorithms.

Findings

Outperforms baseline algorithms on crystal image datasets.

Successfully segments overlapping objects of multiple shapes.

Accurately classifies shape identities of segmented objects.

Abstract

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.