Quantitative Statistics and Identification of Tool-Marks
| Autor: | Min Yang, Yi-Ming Fu, Jiangfeng Wang, Li Mou, Yu Wang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
business.product_category
Computer science Local binary patterns business.industry 010401 analytical chemistry Pattern recognition 01 natural sciences 0104 chemical sciences Pathology and Forensic Medicine Pliers Random forest Image (mathematics) 03 medical and health sciences Identification (information) 0302 clinical medicine Tool marks Operator (computer programming) Feature (computer vision) Genetics 030216 legal & forensic medicine Artificial intelligence business |
| Zdroj: | Journal of forensic sciencesReferences. 64(5) |
| ISSN: | 1556-4029 |
| Popis: | This study was designed to establish a feature identification method of tool-mark 2D data. A uniform local binary pattern histogram operator was developed to extract the tool-mark features, and the random forest algorithm was adopted to identify these. The presented method was used to conduct five groups of experiments with a 2D dataset of known matched and nonmatched tool-marks made by bolt clippers, cutting pliers, and screwdrivers. The experimental results show that the proposed method achieved a high rate of identification of the tool-mark samples generated under identical conditions. The proposed method effectively overcomes the disadvantage of unstable illumination of 2D tool-mark image data and avoids the difficulty in mark inspection caused by manually preset parameters in the existing methods, thus reducing the uncertainty of inspected results. |
| Databáze: | OpenAIRE |
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