Image analysis for blood spatter problems - Nusrat Jahan Shoumy (School of Computer and Communication Engineering, Universiti Malaysia Perlis, 2015)
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) andFunction Fitting Neural Network (FFNN) is proposed.