Campus Units

Center for Statistics and Applications in Forensic Evidence (CSAFE)"

Document Type

Article

Publication Version

Published Version

Publication Date

5-4-2021

Journal or Book Title

Pattern Analysis and Applications

First Page

1

Last Page

14

DOI

10.1007/s10044-020-00951-z

Abstract

Recognition of overlapping objects is required in many applications in the field of computer vision. Examples include cell segmentation, bubble detection and bloodstain pattern analysis. This paper presents a method to identify overlapping objects by approximating them with ellipses. The method is intended to be applied to complex-shaped regions which are believed to be composed of one or more overlapping objects. The method has two primary steps. First, a pool of candidate ellipses are generated by applying the Euclidean distance transform on a compressed image and the pool is filtered by an overlaying method. Second, the concave points on the contour of the region of interest are extracted by polygon approximation to divide the contour into segments. Then, the optimal ellipses are selected from among the candidates by choosing a minimal subset that best fits the identified segments. We propose the use of the adjusted Rand index, commonly applied in clustering, to compare the fitting result with ground truth. Through a set of computational and optimization efficiencies, we are able to apply our approach in complex images comprised of a number of overlapped regions. Experimental results on a synthetic data set, two types of cell images and bloodstain patterns show superior accuracy and flexibility of our method in ellipse recognition, relative to other methods.

Comments

The following article is published as Zou, Tong, Tianyu Pan, Michael Taylor, and Hal Stern. "Recognition of overlapping elliptical objects in a binary image." Pattern Analysis and Applications (2021): 1-14. Posted with permission of CSAFE.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

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