Mr. Robert Fischer Profile

Robert Fischer

at Brandenburg University of Applied Sciences

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Publications (4)

Proceedings Article | 17 March 2015 Paper

Towards automated firearm identification based on high resolution 3D data: rotation-invariant features for multiple line-profile-measurement of firing pin shapes

Robert Fischer, Claus Vielhauer

Proceedings Volume 9393, 93930Q (2015) https://doi.org/10.1117/12.2077567

KEYWORDS: Firearms, Weapons, Data acquisition, 3D acquisition, Microchannel plates, Pattern recognition, Feature extraction, Confocal microscopy, 3D image processing, Forensic science

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Understanding and evaluation of potential evidence, as well as evaluation of automated systems for forensic examinations currently play an important role within the domain of digital crime scene analysis. The application of 3D sensing and pattern recognition systems for automatic extraction and comparison of firearm related tool marks is an evolving field of research within this domain. In this context, the design and evaluation of rotation-invariant features for use on topography data play a particular important role. In this work, we propose and evaluate a 3D imaging system along with two novel features based on topography data and multiple profile-measurement-lines for automatic matching of firing pin shapes. Our test set contains 72 cartridges of three manufactures shot by six different 9mm guns. The entire pattern recognition workflow is addressed. This includes the application of confocal microscopy for data acquisition, preprocessing covers outlier handling, data normalization, as well as necessary segmentation and registration. Feature extraction involves the two introduced features for automatic comparison and matching of 3D firing pin shapes. The introduced features are called ‘Multiple-Circle-Path’ (MCP) and ‘Multiple-Angle-Path’ (MAP). Basically both features are compositions of freely configurable amounts of circular or straight path-lines combined with statistical evaluations. During the first part of evaluation (E₁), we examine how well it is possible to differentiate between two 9mm weapons of the same mark and model. During second part (E₂), we evaluate the discrimination accuracy regarding the set of six different 9mm guns. During the third part (E₃), we evaluate the performance of the features in consideration of different rotation angles. In terms of E₁, the best correct classification rate is 100% and in terms of E₂ the best result is 86%. The preliminary results for E₃ indicate robustness of both features regarding rotation. However, in future work these results have to be validated using an enlarged test set.

Proceedings Article | 22 March 2013 Paper

Ballistic examinations based on 3D data: a comparative study of probabilistic Hough Transform and geometrical shape determination for circle-detection on cartridge bottoms

Robert Fischer, Claus Vielhauer, Mario Hildebrandt, Stefan Kiltz, Jana Dittmann

Proceedings Volume 8665, 86650F (2013) https://doi.org/10.1117/12.2004283

KEYWORDS: Hough transforms, Forensic science, Weapons, Firearms, Image processing, Microscopes, Image segmentation, Manufacturing, Confocal microscopy, Detection and tracking algorithms

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The application of contact-less optical 3D sensing techniques yielding digital data for the acquisition of toolmarks on forensic ballistic specimens found at crime scenes, as well as the development of computer-aided, semi-automated firearm identification systems that are using 3D information, are currently emerging fields of research with rising importance. Traditionally, the examination of forensic ballistic specimen is done manually by highly skilled forensic experts using comparison microscopes. A partly automation of the comparison task promises examination results that are less dependent on subjective expertise and furthermore a reduction of the manual work needed. While there are some partly automated systems available they are all of proprietary nature to our current knowledge. One necessary requirement for the examination of forensic ballistic specimens is a reliable circle-detection and segmentation of cartridge bottoms. This information is later used for example for alignment and registration tasks, determination of regions of interest, and locally restricted application of complex feature-extraction algorithms. In this work we are using a Keyence VK-X 105 laser-scanning confocal microscope to acquire a very high detail topography image, a laserintensity image, and a color image of the assessed cartridge bottoms simultaneously. The work is focused on a comparison of Hough Transform (21HT) and Geometric Shape Determination for circle-detection on cartridge bottoms using 3D as well as 2D information. We compare the pre-processing complexity, the required processing time, and the ability for a reliable detection of all desired circles. We assume that the utilization of Geometric Shape Detection can reduce the required processing time due to a less complex processing. For application of shape determination as well as for Hough Transform we expect a more reliable circle-detection when using additional 3D information. Our first experimental evaluation, using 100 9mm center fire cartridges shot from 3 different firearms shows positive tendency to verify these suppositions.

Proceedings Article | 4 May 2012 Paper

Advanced techniques for latent fingerprint detection and validation using a CWL device

Andrey Makrushin, Mario Hildebrandt, Robert Fischer, Tobias Kiertscher, Jana Dittmann, Claus Vielhauer

Proceedings Volume 8436, 84360V (2012) https://doi.org/10.1117/12.922235

KEYWORDS: Forensic science, Sensors, Metals, Statistical analysis, Ultraviolet radiation, Chemical analysis, Feature extraction, Data modeling, Visualization, Inspection

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Proceedings Article | 2 May 2012 Paper

Surface classification and detection of latent fingerprints based on 3D surface texture parameters

Stefan Gruhn, Robert Fischer, Claus Vielhauer

Proceedings Volume 8436, 84361C (2012) https://doi.org/10.1117/12.922772

KEYWORDS: Forensic science, Image classification, Sensors, Data acquisition, Process modeling, Colorimetry, Criminalistics, 3D image processing, Scene classification, Image resolution

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In the field of latent fingerprint detection in crime scene forensics the classification of surfaces has importance. A new method for the scientific analysis of image based information for forensic science was investigated in the last years. Our image acquisition based on a sensor using Chromatic White Light (CWL) with a lateral resolution up to 2 μm. The used FRT-MicroProf 200 CWL 600 measurement device is able to capture high-resolution intensity and topography images in an optical and contact-less way. In prior work, we have suggested to use 2D surface texture parameters to classify various materials, which was a novel approach in the field of criminalistic forensic using knowledge from surface appearance and a chromatic white light sensor. A meaningful and useful classification of different crime scene specific surfaces is not existent. In this work, we want to extend such considerations by the usage of fourteen 3D surface parameters, called 'Birmingham 14'. In our experiment we define these surface texture parameters and use them to classify ten different materials in this test set-up and create specific material classes. Further it is shown in first experiments, that some surface texture parameters are sensitive to separate fingerprints from carrier surfaces. So far, the use of surface roughness is mainly known within the framework of material quality control. The analysis and classification of the captured 3D-topography images from crime scenes is important for the adaptive preprocessing depending on the surface texture. The adaptive preprocessing in dependency of surface classification is necessary for precise detection because of the wide variety of surface textures. We perform a preliminary study in usage of these 3D surface texture parameters as feature for the fingerprint detection. In combination with a reference sample we show that surface texture parameters can be an indication for a fingerprint and can be a feature in latent fingerprint detection.

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