Ballistic Specimen Clustering with Toolmark Alignment Consistency
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Solution Overview
Problem
Existing automated ballistic identification systems struggle to effectively cluster ballistic specimens when dealing with challenging cases, such as deformed or fragmented bullets, and cartridge cases of different compositions, as they often merge or split clusters inappropriately due to insufficient handling of similarity scores and clustering thresholds.
Innovation Solution
A method and system that utilize topographic data to determine optimal toolmark alignment parameters, including planar rotations, translations, and consistency measures, to generate clusters of ballistic specimens, using a modified hierarchical agglomerative clustering algorithm that incorporates consistency scores to handle complex cases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If score-based clustering algorithms are used to group ballistic specimens, then clustering can be performed automatically, but the algorithms cannot handle difficult specimens correctly, leading to inappropriate merging or splitting of clusters
Solution Approach 1:
The patent introduces consistency measures as an intermediary factor that mediates between similarity scores and clustering decisions. These consistency measures evaluate whether the optimal alignment parameters are consistent across multiple specimens, providing an additional criterion that resolves ambiguous clustering cases and improves reliability while maintaining automation.
2Adaptability or versatility
If clustering thresholds are adjusted to handle difficult specimens, then more specimens can be grouped, but this leads to merging clusters that should be separate or splitting valid clusters
Solution Approach 1:
The patent changes the parameter used for clustering decisions from solely similarity scores to a combination of similarity scores and consistency measures. This parameter change allows the system to maintain appropriate clustering thresholds while using consistency measures to handle difficult specimens, preventing inappropriate merging or splitting.
3Device complexity
If only pairwise similarity scores are used for clustering, then the analysis is simpler, but it cannot adequately handle cases with more than two specimens
Solution Approach 1:
The patent adds another dimension to the analysis by introducing consistency measures that evaluate triplet relationships between specimens. This dimensional extension from pairwise to triplet-level analysis enables the system to handle multi-specimen cases appropriately while maintaining computational feasibility through structured approaches.
Data Source
AI summary
There are described a method and a system for generating clusters of ballistic specimens. Using an image acquisition tool, topographic data for at least three ballistic specimens of at least one region of interest is acquired. From the topographic data, at least one parameter characterizing optimal toolmark alignment is determined for every distinct pair of the at least three ballistic specimens, each ballistic specimen having a plurality of toolmarks formed thereon. From the topographic data, at least one pairwise similarity score associated with optimal toolmark alignment is determined for every distinct pair of the at least three ballistic specimens. At least one triplet-wise consistency measure indicative of a consistency of optimal toolmark alignment is determined for every distinct triplet of the at least three ballistic specimens. A cluster analysis is conducted based on the at least one similarity score and the at least one consistency measure to generate the clusters.