Authors:
Kumar Abhinav
;
Jaideep Singh Chauhan
and
Debasis Sarkar
Affiliation:
Indian Institute of Technology Kharagpur, India
Keyword(s):
Image Segmentation, Overlapping Objects, Multiple Shaped Objects, Contour Grouping, Concave Points.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Segmentation and Grouping
;
Shape Representation and Matching
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.