  Recent developments in image quality, data storage, and computational
capacity have heightened the need for texture analysis in image process. To
date various methods have been developed and introduced for assessing textures
in images. One of the most popular texture analysis methods is the Texture
Energy Measure (TEM) and it has been used for detecting edges, levels, waves,
spots and ripples by employing predefined TEM masks to images. Despite several
success- ful studies, TEM has a number of serious weaknesses in use. The major
drawback is; the masks are predefined therefore they cannot be adapted to
image. A new method, Adaptive Texture Energy Measure Method (aTEM), was offered
to over- come this disadvantage of TEM by using adaptive masks by adjusting the
contrast, sharpening and orientation angle of the mask. To assess the
applicability of aTEM, it is compared with TEM. The accuracy of the
classification of butterfly, flower seed and Brodatz datasets are 0.08, 0.3292
and 0.3343, respectively by TEM and 0.0053, 0.2417 and 0.3153, respectively by
aTEM. The results of this study indicate that aTEM is a successful method for
texture analysis.
