通过使用3D灰色级别共发生矩阵在盒装中增强测量异常的检测
Yiyun Gan1, Linyu Huang1, Qian Ning1
1College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan 610065, China.
Forensic science international
|January 14, 2025
概括
这项研究引入了一种使用3D纹理分析的新自动化方法,用于检测弹外上发射针印记中的异常. 该技术通过识别测量错误,显著提高了法医证据分析的可靠性.
科学领域:
- 法医科学 法医科学 法医科学
- 计量学 计量学 计量学
- 计算机视觉 计算机视觉
背景情况:
- 开枪针的印记是重要的法医证据.
- 传统分析有其局限性,包括对证据的潜在损害.
- 激光移位传感器提供3D痕迹捕获,但在金属表面面临挑战.
研究的目的:
- 开发一种自动化方法来检测3D发射针印记痕迹中的测量异常.
- 为了克服从曲的反射弹外表面的反光反射所带来的挑战.
- 提高法医科学中自动化痕迹分析的可靠性.
主要方法:
- 使用线路激光移位传感器进行3D痕迹采集.
- 扩展了灰色级别共发生矩阵 (GLCM) 用于3D纹理特征提取.
- 使用支持矢量机 (SVM) 进行异常检测和分类.
主要成果:
- 实现了98.92%的测量异常检测精度.
- 证明了3D GLCM纹理分析用于识别异常的有效性.
- 通过对2038组发射针印记数据验证了拟议的自动检测方法.
结论:
- 拟议的自动化方法有效地检测3D发射针印记痕迹中的异常.
- 这种方法提高了法医证据分析的可靠性.
- 建议在法医实验室广泛采用,以提高证据的完整性.
更多相关视频
06:41A New Technique for Quantitative Analysis of Hair Loss in Mice Using Grayscale Analysis
Published on: March 9, 2015
8.9K
10:10Three-Dimensional Particle Shape Analysis Using X-ray Computed Tomography: Experimental Procedure and Analysis Algorithms for Metal Powders
Published on: December 4, 2020
1.8K
相关概念视频
Detection of Black Holes
1.7K
Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
1.7K
Detection of Gross Error: The Q Test
7.1K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
7.1K
Difference from Background: Limit of Detection
9.0K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
The LOD indicates the presence or absence...
9.0K
