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 Information: Journal of Visual Communication and Image Representation
Volume number: 73
ISSN: 1047-3203
eISSN: 1095-9076
JUFO-Level of this publication: 2
Open Access: Open Access hybrid publication

Abstract

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 2020-30-11 at 10:45

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