A computer vision approach for automated analysis and classification of microstructural image data
A computer vision approach for automated analysis and classification of microstructural image data
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DOI：
10.1016/j.commatsci.2015.08.011
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发表时间：
2015-12-01
影响因子：
3.3
通讯作者：
Holm, Elizabeth A.
中科院分区：
文献类型：
--
作者：
DeCost, Brian L.;Holm, Elizabeth A.
应用“视觉特征袋”图像表示来创建通用显微结构签名，其可用于自动地在大型且不同的显微结构图像数据集中找到关系。使用这种表示，支持向量机（SVM）进行了训练，以将微结构分类为七组之一，在5倍交叉验证中具有大于80%的准确度。此外，包的视觉特征被实现为基础的视觉搜索引擎，确定最佳匹配的查询图像在数据库中的微结构。这些新的应用展示了计算机视觉概念在微结构科学中的潜力和局限性。(C)2015年，作者。由爱思唯尔公司出版
The 'bag of visual features' image representation was applied to create generic microstructural signatures that can be used to automatically find relationships in large and diverse microstructural image data sets. Using this representation, a support vector machine (SVM) was trained to classify microstructures into one of seven groups with greater than 80% accuracy over 5-fold cross validation. In addition, the bag of visual features was implemented as the basis for a visual search engine that determines the best matches for a query image in a database of microstructures. These novel applications demonstrate the potential and the limitations of computer vision concepts in microstructural science. (C) 2015 The Authors. Published by Elsevier B.V.