ดูข้อมูลเพิ่มเติมได้ในเอกสาร 2 ฉบับ ได้แก่ Haralick et. al, "Textural Features for Image Classification", https://doi.org/10.1109/TSMC.1973.4309314 และ Conners, et al, "Segmentation of a high-resolution urban scene using texture operators", https://doi.org/10.1016/0734-189X(84)90197-X
[[["เข้าใจง่าย","easyToUnderstand","thumb-up"],["แก้ปัญหาของฉันได้","solvedMyProblem","thumb-up"],["อื่นๆ","otherUp","thumb-up"]],[["ไม่มีข้อมูลที่ฉันต้องการ","missingTheInformationINeed","thumb-down"],["ซับซ้อนเกินไป/มีหลายขั้นตอนมากเกินไป","tooComplicatedTooManySteps","thumb-down"],["ล้าสมัย","outOfDate","thumb-down"],["ปัญหาเกี่ยวกับการแปล","translationIssue","thumb-down"],["ตัวอย่าง/ปัญหาเกี่ยวกับโค้ด","samplesCodeIssue","thumb-down"],["อื่นๆ","otherDown","thumb-down"]],["อัปเดตล่าสุด 2026-04-20 UTC"],[],["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"]]