如需了解详情,请参阅以下两篇论文:Haralick 等人撰写的“Textural Features for Image Classification”,https://doi.org/10.1109/TSMC.1973.4309314;以及 Conners 等人撰写的“Segmentation of a high-resolution urban scene using texture operators”,https://sdoi.org/10.1016/0734-189X(84)90197-X。
[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["没有我需要的信息","missingTheInformationINeed","thumb-down"],["太复杂/步骤太多","tooComplicatedTooManySteps","thumb-down"],["内容需要更新","outOfDate","thumb-down"],["翻译问题","translationIssue","thumb-down"],["示例/代码问题","samplesCodeIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC):2025-07-26。"],[],["This content describes the computation of texture metrics using the Gray Level Co-occurrence Matrix (GLCM). It calculates 18 metrics, including Angular Second Moment, Contrast, Correlation, and Entropy, among others. The GLCM tabulates pixel brightness combinations within an image, considering direction and distance. Input images must be integer-valued. The `Image.glcmTexture` function takes `size`, `kernel` (pixel offsets), and `average` (directional averaging) as parameters. Output is 18 bands per input band, either averaged or per directional pair in the kernel.\n"]]