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

机译:自动子弹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,使用从土地印象中提取的压缩签名轮廓的横相关函数来测量不同符号之间的突变标记的相似性。包含桶形特征弱横向横向横向横向划分的无效区域可能导致签名型材的次优,并随后对相关结果的劣化。在本文中,提出了一种用于定位基于边缘检测技术的偏振标记和选择有效相关区域的方法,以获得压缩签名简档的最佳提取。从12枪桶中射出的48个子弹的实验结果已经表现出比以前的研究结果更高的正确匹配速率,而没有相关区域选择处理。此外,为了减少存储空间并增加相关速度,尝试将具有多个z量化(或灰度)水平转换为二进制轮廓的传统轮廓。

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