Image analysis for blood spatter problems

Violent crime scenes are becoming increasingly common nowadays. Usually in such cases, the obvious evidence of the crime is blood spatter. Forensic specialists try to predict the event of the crime as accurately as possible based on blood spatter evidence in the scene. Recently, programs have also b...

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Main Author:	Nusrat Jahan, Shoumy
Format:	Thesis
Language:	English
Subjects:	Image analysis Blood spatter Evidence Forensic Crime scene evidence
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/61530/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/61530/2/Full%20text.pdf
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spelling	my-unimap-615302019-08-22T06:31:00Z Image analysis for blood spatter problems Nusrat Jahan, Shoumy Dr. Shahrul Nizam Yaakob Violent crime scenes are becoming increasingly common nowadays. Usually in such cases, the obvious evidence of the crime is blood spatter. Forensic specialists try to predict the event of the crime as accurately as possible based on blood spatter evidence in the scene. Recently, programs have also been developed to predict the events in the crime scene. However, there are several shortcomings including predicting the source of origin and trajectory of the blood drop, complications from large amount of manual input and lack of research on related classification methods, such as Neural Network (NN) in this field. In this thesis, focus is given to enhance the prediction method both theoretically and practically. The proposed theoretical model is based on the Newton’s Law for linear blood spatter drop in motion, assuming the motion has drag. It produces more accurate results compared to the model using Stokes’ Law, which has been used in previous researches, if blood droplet radius is more than 2 mm, otherwise they are comparable. To perform experimental research, a number of available blood stain image data is necessary, but there is no available data. Hence, a database (DB) with 1252 blood stain images has been created through the formation of synthetic blood formula and practical bloodletting crime image scenario. Finally, the classification and automation for the reconstruction of blood droplet trajectory using two different Neural Networks (NN) modules which are Cascade Forward Neural Network (CFNN) and Function Fitting Neural Network (FFNN) is proposed. The CFNN and FFNN then tested with the developed image data-set. FFNN exhibits in average 91.1% classification accuracy for blood stain images, which is 4.5% better than CFNN and significantly better than previous researches. The proposed system may help forensic investigators to acquire crime scene evidence in an easy, faster and reliable way in near future. Universiti Malaysia Perlis (UniMAP) 2015 Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/61530 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/61530/1/Page%201-24.pdf 6d4b1caa6100e06cca9b3c13d76d6632 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/61530/2/Full%20text.pdf f980c2eec8ba9dc225c626f6e65d6196 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/61530/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 Image analysis Blood spatter Evidence Forensic Crime scene evidence School of Computer and Communication Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Dr. Shahrul Nizam Yaakob
topic	Image analysis Blood spatter Evidence Forensic Crime scene evidence
spellingShingle	Image analysis Blood spatter Evidence Forensic Crime scene evidence Nusrat Jahan, Shoumy Image analysis for blood spatter problems
description	Violent crime scenes are becoming increasingly common nowadays. Usually in such cases, the obvious evidence of the crime is blood spatter. Forensic specialists try to predict the event of the crime as accurately as possible based on blood spatter evidence in the scene. Recently, programs have also been developed to predict the events in the crime scene. However, there are several shortcomings including predicting the source of origin and trajectory of the blood drop, complications from large amount of manual input and lack of research on related classification methods, such as Neural Network (NN) in this field. In this thesis, focus is given to enhance the prediction method both theoretically and practically. The proposed theoretical model is based on the Newton’s Law for linear blood spatter drop in motion, assuming the motion has drag. It produces more accurate results compared to the model using Stokes’ Law, which has been used in previous researches, if blood droplet radius is more than 2 mm, otherwise they are comparable. To perform experimental research, a number of available blood stain image data is necessary, but there is no available data. Hence, a database (DB) with 1252 blood stain images has been created through the formation of synthetic blood formula and practical bloodletting crime image scenario. Finally, the classification and automation for the reconstruction of blood droplet trajectory using two different Neural Networks (NN) modules which are Cascade Forward Neural Network (CFNN) and Function Fitting Neural Network (FFNN) is proposed. The CFNN and FFNN then tested with the developed image data-set. FFNN exhibits in average 91.1% classification accuracy for blood stain images, which is 4.5% better than CFNN and significantly better than previous researches. The proposed system may help forensic investigators to acquire crime scene evidence in an easy, faster and reliable way in near future.
format	Thesis
author	Nusrat Jahan, Shoumy
author_facet	Nusrat Jahan, Shoumy
author_sort	Nusrat Jahan, Shoumy
title	Image analysis for blood spatter problems
title_short	Image analysis for blood spatter problems
title_full	Image analysis for blood spatter problems
title_fullStr	Image analysis for blood spatter problems
title_full_unstemmed	Image analysis for blood spatter problems
title_sort	image analysis for blood spatter problems
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Computer and Communication Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/61530/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/61530/2/Full%20text.pdf
_version_	1747836842387963904

Image analysis for blood spatter problems

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