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Digitized crime scene forensics: Automated trace separation of toolmarks on high-resolution 2D/3D CLSM surface data

机译:数字化犯罪现场取证:高分辨率2D / 3D CLSM表面数据的自动跟踪工具标记分离

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Locksmith forensics is an important and very challenging part of classic crime scene forensics. In prior work, we propose a partial transfer to the digital domain, to effectively support forensic experts and present approaches for a full process chain consisting of five steps: Trace positioning, 2D/3D acquisition with a confocal 3D laser scanning microscope, detection by segmentation, trace type determination, and determination of the opening method. In particular the step of trace segmentation on high-resolution 3D surfaces thereby turned out to be the part most difficult to implement. The reason for that is the highly structured and complex surfaces to be analyzed. These surfaces are cluttered with a high number of toolmarks, which overlap and distort each other. In Clausing et al., we present an improved approach for a reliable segmentation of relevant trace regions but without the possibility of separating single traces out of segmented trace regions. However, in our past research, especially features based on shape and dimension turned out to be highly relevant for a fully automated analysis and interpretation. In this paper, we consequently propose an approach for this separation. To achieve this goal, we use our segmentation approach and expand it with a combination of the watershed algorithm with a graph-based analysis. Found sub-regions are compared based on their surface character and are connected or divided depending on their similarity. We evaluate our approach with a test set of about 1,300 single traces on the exemplary locking cylinder component 'key pin' and thereby are able of showing the high suitability of our approach.
机译:锁匠取证是经典犯罪现场取证的重要且非常具有挑战性的一部分。在事先工作中,我们提出了对数字领域的部分转移,以有效地支持法医专家和现有过程链的现有方法,包括五个步骤:跟踪定位,2D / 3D采集与共焦3D激光扫描显微镜,通过分割检测,跟踪类型确定,以及开口方法的确定。特别地,在高分辨率3D表面上进行跟踪分割的步骤,从而成为最难以实现的部分。这是要分析的高度结构化和复杂的表面。这些表面具有大量的工具标记,其重叠并抗扭。在Clausing等人中,我们提出了一种改进的方法,以获得相关痕量区域的可靠分割,但没有可能将单个痕迹分离出分段的痕迹区域。然而,在我们过去的研究中,特别是基于形状和维度的特征,对全自动分析和解释具有高度相关。在本文中,我们因此提出了一种用于这种分离的方法。为实现这一目标,我们使用我们的分割方法并将其与流域算法的组合进行扩展,具有基于图形的分析。找到基于它们的表面特征的比较子区域,并且根据其相似性连接或划分。我们在示例性锁定缸组件“键销”上使用约1,300个单个迹线的测试集评估我们的方法,从而能够显示我们的方法的高适用性。

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