Recognition of overlapping elliptical objects in a binary image

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 i...

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Bibliographic Details
Published in:Pattern analysis and applications : PAA 2021-08, Vol.24 (3), p.1193-1206
Main Authors: Zou, Tong, Pan, Tianyu, Taylor, Michael, Stern, Hal
Format: Article
Language:English
Subjects:
Approximation
Clustering
Computer Science
Computer vision
Contours
Ellipses
Euclidean geometry
Identification methods
Image segmentation
Object recognition
Optimization
Pattern analysis
Pattern Recognition
Theoretical Advances
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Summary: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.
ISSN:1433-7541
1433-755X
DOI:10.1007/s10044-020-00951-z