科研之友
微信
新浪微博
Facebook
分享链接
摘要
The striations on bullet surface are 3D micro structures formed when a bullet is forcing its way out of barrel. Each barrel leaves individual striation patterns on bullets. Hence, the striation information of bullets is helpful for firearm identification. Common automatic identification methods process these images using linear time invariant (LTI) filters based on correlation. These methods do not consider the sensitivity of correlation based comparisons to nonlinear baseline drifts. The striations are undeniably random unique micro structures caused by random non-model-based imperfections in the tools used in rifling process, therefore any characteristic profile that is extracted from a bullet image is statistically non-stationary. Due to limitations of LTI filters, using them in smoothing bullet images and profiles may cause information loss and impact the process of identification. To address these problems, in this article, we consider bullet images as nonlinear non-stationary processes and propose a novel method which uses ensemble empirical mode decomposition (EEMD) as a preprocessing algorithm for smoothing and feature extraction. The features extracted by EEMD algorithm not only contain less noise, but also have no nonlinear baseline drifts. These improvements help the correlation based comparison methods to perform more robustly and efficiently. The experiments showed that our proposed method attained better results compared with two common methods in the field of automatic bullet identification.
- 出版日期2017-9
