A dataset for evaluating blood detection in hyperspectral images.

The sensitivity of imaging spectroscopy to haemoglobin derivatives makes it a promising tool for detecting blood. However, due to complexity and high dimensionality of hyperspectral images, the development of hyperspectral blood detection algorithms is challenging. To facilitate their development, we present a new hyperspectral blood detection dataset.

Blood Stain Classification with Hyperspectral Imaging and Deep Neural Networks.

In recent years, growing interest in deep learning neural networks has raised a question on how they can be used for effective processing of high-dimensional datasets produced by hyperspectral imaging (HSI). HSI, traditionally viewed as being within the scope of remote sensing, is used in non-invasive substance classification. One of the areas of potential application is forensic science, where substance classification on the scenes is important.

Application of hyperspectral imaging and machine learning methods for the detection of gunshot residue patterns.

Advanced image processing algorithms can support the forensic analyst to make tasks like detection, pattern comparison or identification more objective. In the case of the gunshot residue (GSR) analysis, the automatic detection of potential GSR samples can support the task of evidence collection or analysis of residue needed e.g.