Volume 3, Issue 2, April 2014, Page: 13-18
Adaptive Texture Energy Measure Method
Ömer Faruk Ertuğrul, Electrical and Electronics Engineering, Batman University, Batman, Turkey
Received: May 26, 2014;       Accepted: Jun. 13, 2014;       Published: Jun. 30, 2014
DOI: 10.11648/j.ijiis.20140302.11      View  2860      Downloads  128
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 successful 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 overcome 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.
Texture Energy Measure, Adaptive Image Process, Machine Vision, Feature Extraction
To cite this article
Ömer Faruk Ertuğrul, Adaptive Texture Energy Measure Method, International Journal of Intelligent Information Systems. Vol. 3, No. 2, 2014, pp. 13-18. doi: 10.11648/j.ijiis.20140302.11
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