Mr. Christoph Brein Profile

Christoph Brein

at Univ Karlsruhe

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Proceedings Article | 14 March 2005 Paper

Segmentation of cartridge cases based on illumination and focus series

Christoph Brein

Proceedings Volume 5685, (2005) https://doi.org/10.1117/12.585763

KEYWORDS: Image segmentation, Distance measurement, Hough transforms, Weapons, Forensic science, Image quality, Feature extraction, Edge detection, Optical inspection, Image processing

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Cartridge cases are important forensic specimen for the identification of weapons. The illumination conditions in the area of the firing pin marks and the breech face marks are very different and have to be treated separately to achieve an appropriate image quality for a visual inspection. Furthermore, not only the comparison but also the detection of the different and independent forensic marks should be automated. Both problems lead to the task of segmenting the different parts of the cartridge case bottom. In this paper, two automated approaches for the segmentation of cartridge cases are investigated and compared. The aim of the segmentation is the detection of the cartridge case border, the primer, the firing pin mark and additionally the letters around the primer. The first approach uses images obtained under systematically varied illumination conditions. After a preprocessing step a circle detection is applied to find the circular structures. The analysis of illumination series combined with a the connected components labeling method detect the letters. In a second approach, the depth-from-focus method is used to obtain 2½ D-data. This data is segmented applying a plane estimation technique. This results directly in the detection of the letters. Afterwards a circle detection algorithm identifies the parameters of the circular structures. With the introduced methods it is possible to optimize the illumination in order to realize a higher contrast of both the striation marks on the cartridge case surface and of the indentation of the firing pin independently. The improved image quality helps the examiner in identifying weapons and will help to improve the automated comparison strategies.

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