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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| Format: | Thesis |
| Language: | English |
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| 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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| Summary: | 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. |
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