CSAFE researchers are developing more-precise algorithms with fluid dynamics theory and inverse statistical methods to increase blood pattern analysis accuracy.
Statistical Learning, Fluid Dynamics, Pattern Recognition
- Collecting and organizing a database of high-quality blood spatter videos and images.
Developing a physics-based algorithm to predict the region of origin of bloodstains by inspection of a blood spatter.
- Applying software tools to automatically recognize blood stains from a blood spatter image and extract image features to reconstruct trajectory.
- Developing software tools that can be used to study the trajectory of a blood drop. CSAFE tools break down a segment of blood spatter video into frames and give a mathematical description to both the drop of blood and to its location in the image.
Benefits of Research
The high resolution blood spatter database has been built by using controlled and carefully documented experiments. This databased will be shared with researchers worldwide so that every group can test their backward trajectory reconstruction methods, to identify the region of origin of the blood spatter, and to determine the blood pattern generation mechanism with greater ease and accuracy.