Ellipse Detection in Forensic Blood Stain Images Analysis

Wójtowicz, Tomasz; Bułka, Dariusz

doi:10.4149/cai_2018_1_213

Cited by 1 publication

(2 citation statements)

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“…Another class of contour-based methods aims to identify the concave area of the contour, based on which the contour is segmented and then fitted by ellipses. In [17], Zafari et al categorized concave point detection methods into four groups: curvature [2,18], skeleton, chord [6,19] and polygon approximation [3,5,7,15]. They showed via experiment that polygon approximation methods outperform the others.…”

Section: Related Workmentioning

confidence: 99%

“…Recognizing overlapping objects is a common problem in image analysis and arises in various real-world applications, such as splitting touching cells in medical images [1][2][3][4], bubble detection and recognition [5,6] and bloodstain pattern analysis in forensic science [7]. In cases where the individual objects have approximately oval shapes (cells, bubbles or bloodstains), one approach is to find a representation composed of multiple ellipses to approximate the overlapping objects.…”

Section: Introductionmentioning

confidence: 99%

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Recognition of overlapping elliptical objects in a binary image

Zou

Pan

Taylor

2021

Pattern Anal Applic

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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.

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