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Optimal Compression and Binarization of Signature Profiles for Automated Bullet Identification Systems

机译:自动项目符号识别系统的签名配置文件的最佳压缩和二值化

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In some automated bullet identification systems, the similarity of striation marks between different bullets is measured using the cross correlation function of the compressed signature profile extracted from a land impression. Inclusion of invalid areas weakly striated by barrel features may lead to sub-optimal extraction of the signature profile and subsequent deterioration of correlation results. In this paper, a method for locating striation marks and selecting valid correlation areas based on an edge detection technique is proposed for the optimal extraction of the compressed signature profiles. Experimental results from correlating 48 bullets fired from 12 gun barrels of 6 manufacturers have demonstrated a higher correct matching rate than the previous study results without correlation area selection processing. Furthermore, an attempt to convert a traditional profile with multiple z-quantization (or gray scale) levels into a binary profile is made for the purpose of reducing storage space and increasing correlation speed.
机译:在一些自动子弹识别系统中,使用从陆印中提取的压缩签名轮廓的互相关函数来测量不同子弹之间的条纹标记的相似性。包含由桶形特征弱条纹的无效区域可能会导致签名配置文件的提取不够理想,从而导致相关结果的恶化。本文提出了一种基于边缘检测技术的条纹标记定位和有效相关区域选择方法,以优化压缩特征谱的提取。关联从6个制造商的12个枪管发射的48颗子弹的实验结果表明,与没有关联区域选择处理的先前研究结果相比,正确匹配率更高。此外,出于减小存储空间和提高相关速度的目的,尝试将具有多个z-量化(或灰度)级的传统配置文件转换为二进制配置文件。

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