Crime Scene Representation (2D, 3D, Stereoscop Projection) and Classification

In this paper we provide a study about crime scenes and its features used in criminal investigations. we argue that the crime scene provides a large set of features that can be used to corroborate the conclusions emitted by the experts. we also propose a set of features to classify the violent crime considering two classes: attack from inside or outside of the scene. the classification stage is based on conventional mlp (multiple-layer perceptron) neural network and svm (support vector machine). the experimental results reveal an error rate of 30.3% (mlp), 22.8% (svm-linear), and 19.4% (svm-polynomial) using a database composed of 400 crime scenes. this paper presents an experiment based on a stereoscopic projection that allows to experts analyze and take decisions about the crime scene and its dynamic.