A4 Conference proceedings

Comparison of Concave Point Detection Methods for Overlapping Convex Objects Segmentation

Publication Details
Authors: Zafari Sahar, Eerola Tuomas, Sampo Jouni, Kälviäinen Heikki, Haario Heikki
Publisher: Springer Verlag (Germany): Series
Publication year: 2017
Language: English
Related Journal or Series Information: Lecture Notes in Computer Science
Title of parent publication: Image Analysis: 20th Scandinavian Conference, SCIA 2017
Journal acronym: LNCS
Volume number: 10270
Start page: 245
End page: 256
Number of pages: 12
ISBN: 978-3-319-59125-4
eISBN: 978-3-319-59126-1
ISSN: 0302-9743
eISSN: 1611-3349
JUFO-Level of this publication: 1
Open Access: Not an Open Access publication


Segmentation of overlapping convex objects has gained a lot of attention in numerous biomedical and industrial applications. A partial overlap between two or more convex shape objects leads to a shape with concave edge points that correspond to the intersections of the object boundaries. Therefore, it is a common practice to utilize these concave points to segment the contours of overlapping objects. Although a concave point has a clear mathematical definition, the task of concave point detection (CPD) from noisy digital images with limited resolution is far from trivial. This work provides the first comprehensive comparison of CPD methods with both synthetic and real world data. We further propose a modification to an earlier CPD method and show that it outperforms the other methods. Finally, we demonstrate that by using the enhanced concave points we obtain segmentation results that outperform the state-of-the-art in the task of partially overlapping convex object segmentation.

Last updated on 2018-19-10 at 07:55