A data set of bloodstain patterns for teaching and research in bloodstain pattern analysis: Impact beating spatters

Attinger, Daniel; Liu, Yu; Bybee, Tyler; Brabanter, Kris De

doi:10.1016/j.dib.2018.02.070

Cited by 17 publications

(41 citation statements)

References 13 publications

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“…11 demonstrates that DTECMA has the ability to detect and identify overlapping stains. Similar results have been obtained for other images from [41]. Quantitative assessment of the performance of DTECMA in the bloodstain pattern application is not straightforward.…”

Section: Application To Bloodstain Patternssupporting

confidence: 77%

“…With DTECMA, these overlapping bloodstains can be used to strengthen the analysis. We demonstrate DTECMA on the impact pattern HP31 published in [41]. Pre-processing is applied to the pattern by following the procedures in [39], including background subtraction, element segmentation for binarization and morphological operations for smoothing and hole filling.…”

Section: Application To Bloodstain Patternsmentioning

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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Section: Application To Bloodstain Patternssupporting

confidence: 77%

Section: Application To Bloodstain Patternsmentioning

confidence: 99%

Recognition of overlapping elliptical objects in a binary image

Zou

Pan

Taylor

2021

Pattern Anal Applic

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“…Since the area of the scanner is smaller than the maximum target surface of 1.36 m × 1.1 m used to collect the spatter pattern, scans are performed in a piecewise manner and assembled using graphics editor software Adobe Photoshop ® . The spatter patterns are listed in Table b, and full description of how each pattern was generated is in Refs .…”

Section: Methodsmentioning

confidence: 99%

Automatic Classification of Bloodstain Patterns Caused by Gunshot and Blunt Impact at Various Distances

Liu

Attinger

Brabanter

2020

Journal of Forensic Sciences

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The forensics discipline of bloodstain pattern analysis plays an important role in crime scene analysis and reconstruction. One reconstruction question is whether the blood has been spattered via gunshot or blunt impact such as beating or stabbing. This paper proposes an automated framework to classify bloodstain spatter patterns generated under controlled conditions into either gunshot or blunt impact classes. Classification is performed using machine learning. The study is performed with 94 blood spatter patterns which are available as public data sets, designs a set of features with possible relevance to classification, and uses the random forests method to rank the most useful features and perform classification. The study shows that classification accuracy decreases with the increasing distance between the target surface collecting the stains and the blood source. Based on the data set used in this study, the model achieves 99% accuracy in classifying spatter patterns at distances of 30 cm, 93% accuracy at distances of 60 cm, and 86% accuracy at distances of 120 cm. Results with 10 additional backspatter patterns also show that the presence of muzzle gases can reduce classification accuracy.

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“…New fluid dynamic models of the atomization of blood have been tested against backward [5] and forward [6] gunshot spatters. Medium to large datasets of blood spatters [7] [8] have been made available, in high resolution, to the scientific community for the purpose of testing methods and data. High Speed videos describing the multiple physical phenomena related to BPA were produced [9], and quantitative measurements thereof have estimated the initial velocities and drop sizes [10].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Note that those experiments on how far a blood drop travels were not as extensive as the independent reports of the same cited in the introduction, but were produced within a larger effort focused on creating a data base of freely-available high-resolution spatters [7]. Gunshot experiments are good candidates to produce stains at large distances from their source, because gunshots produce a wide range [10] of drop velocities and drop sizes, including drop sizes and velocities likely to travel maximum distances, as predicted in Figure 7.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Charts based on millions of fluid dynamics simulations provide a simple tool to estimate how far from its source a specific blood stain can be found

Attinger

2019

Forensic Science International

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The bloodstain pattern analyst sometimes has to judge if a given stain could originate from a specific location. A wide range of values of the maximum distance that a blood drop can travel have been reported from experiments, ranging from less than one meter to more than 10 meter. Here we formulate the problem in a fluid dynamics and data mining framework. The fluid dynamics is solved with Newton's classical equation of motion coupled with well-established models for the gravity and drag forces that bend the trajectories of drops. The parameters screened are the drop size, initial velocity and launch angle, as well as the height of a blood source and the ceiling height. Combining a wide range of values of those five parameter commended the performance of more than 5 million fluid dynamic simulations. Results of those simulations have been searched and mined for parameters directly measurable on a crime scene, such as the stain size and stain ellipticity. The results are presented in simple charts. Those charts are easy to use, and do not require any knowledge of fluid dynamics from the analyst. Disciplines Aerodynamics and Fluid Mechanics | Forensic Science and Technology | Mechanical Engineering Comments This is a manuscript of an article published as Attinger, Daniel. "Charts based on millions of fluid dynamics simulations provide a simple tool to estimate how far from its source a specific blood stain can be found."

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A data set of bloodstain patterns for teaching and research in bloodstain pattern analysis: Impact beating spatters

Cited by 17 publications

References 13 publications

Recognition of overlapping elliptical objects in a binary image

Recognition of overlapping elliptical objects in a binary image

Automatic Classification of Bloodstain Patterns Caused by Gunshot and Blunt Impact at Various Distances

Charts based on millions of fluid dynamics simulations provide a simple tool to estimate how far from its source a specific blood stain can be found

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