A1 Journal article (refereed), original research

Resolving overlapping convex objects in silhouette images by concavity analysis and Gaussian process

Open Access hybrid publication

Publication Details

Authors: Zafari Sahar, Murashkina Mariia, Eerola Tuomas, Sampo Jouni, Kälviäinen Heikki, Haario Heikki

Publisher: Elsevier

Publication year: 2020

Language: English

Related journal or series: Journal of Visual Communication and Image Representation

Volume number: 73

ISSN: 1047-3203

eISSN: 1095-9076

JUFO level of this publication: 2

Digital Object Identifier (DOI): http://dx.doi.org/10.1016/j.jvcir.2020.102962

Open Access: Open Access hybrid publication


This paper introduces a novel method for segmentation of clustered partially overlapping convex objects in silhouette images. The proposed method involves three main steps: pre-processing, contour evidence extraction, and contour estimation. Contour evidence extraction starts by recovering contour segments from a binarized image by detecting concave points. After this the contour segments which belong to the same objects are grouped. The grouping is formulated as a combinatorial optimization problem and solved using the branch and bound algorithm. Finally, the full contours of the objects are estimated by a Gaussian process regression method. The experiments on a challenging dataset consisting of nanoparticles demonstrate that the proposed method outperforms three current state-of-art approaches in overlapping convex objects segmentation. The method relies only on edge information and can be applied to any segmentation problems where the objects are partially overlapping and have a convex shape.

Last updated on 2021-16-03 at 12:47