詳情請參閱兩篇論文:Haralick 等人撰寫的「Textural Features for Image Classification」(圖像分類的紋理特徵),網址為 https://doi.org/10.1109/TSMC.1973.4309314;以及 Conners 等人撰寫的「Segmentation of a high-resolution urban scene using texture operators」(使用紋理運算子分割高解析度都市場景),網址為 https://sdoi.org/10.1016/0734-189X(84)90197-X。
[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["缺少我需要的資訊","missingTheInformationINeed","thumb-down"],["過於複雜/步驟過多","tooComplicatedTooManySteps","thumb-down"],["過時","outOfDate","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["示例/程式碼問題","samplesCodeIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間:2025-07-26 (世界標準時間)。"],[],["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"]]