Skip to content

Recognition of Overlapping Elliptical Objects in a Binary Image

Journal: Pattern Analysis and Applications
Published: 2021
Primary Author: Tong Zou
Secondary Authors: Tianyu Pan, Michael Taylor, Hal Stern
Research Area: Bloodstain

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.

Related Resources

Towards a likelihood ratio approach for bloodstain pattern analysis

Towards a likelihood ratio approach for bloodstain pattern analysis

In this work, we explore the application of likelihood ratio as a forensic evidence assessment tool to evaluate the causal mechanism of a bloodstain pattern. It is assumed that there…
A Data-Driven Approach to Model the Generation Process of Bloodstain Patterns

A Data-Driven Approach to Model the Generation Process of Bloodstain Patterns

This poster was presented at the 106th International Association for Identification
Authors' Response to Commentary on: Liu Y, Attinger D, De Brabanter K. Automatic classification of bloodstain patterns caused by gunshot and blunt impact at various distances

Authors' Response to Commentary on: Liu Y, Attinger D, De Brabanter K. Automatic classification of bloodstain patterns caused by gunshot and blunt impact at various distances

The Commentary by A. Keten makes the case for considering evaporation and coagulation in research and applications of the forensic discipline of bloodstain pattern analysis (BPA). The author also mentions…
A Novel, Data-Driven Approach to the Classification of Bloodstain Patterns

A Novel, Data-Driven Approach to the Classification of Bloodstain Patterns

Bloodstain pattern analysis is the study of bloodstains at a crime scene with the purpose of drawing inference about the crime. A typical objective for bloodstain pattern analysis is to…