Feature Textures Extraction of Macroscopic Image of Jatiwood (Tectona Grandy) Based on Gray Level Co-occurence Matrix

Abstract

The features texture in the textured images could be extracted by using grey level co-occurence matrix (GLCM). GLCM was a such kind of good descriptor for textured images, which has two variables in observation window such as angle and distance of pixels. This research observed the results of features texture for four different observation angle in the window of GLCM such as 0°, 45°, 90° and 135°. The object in this reserach was the textured image of macroscopic jati wood (Tectona grandy), a such kind of good wood from Indonesian forest. The extracted texture features were contrast, correlation, energy and homogeneity. The results showed that contrast had a biggest value in direction of 45°, the other features correlation, energy and homogeneity had the biggest value both in direction of 0°.