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.
| Published in | International Journal of Intelligent Information Systems (Volume 3, Issue 2) |
| DOI | 10.11648/j.ijiis.20140302.11 |
| Page(s) | 13-18 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2014. Published by Science Publishing Group |
Texture Energy Measure, Adaptive Image Process, Machine Vision, Feature Extraction
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APA Style
Ömer Faruk Ertuğrul. (2014). Adaptive Texture Energy Measure Method. International Journal of Intelligent Information Systems, 3(2), 13-18. https://doi.org/10.11648/j.ijiis.20140302.11
ACS Style
Ömer Faruk Ertuğrul. Adaptive Texture Energy Measure Method. Int. J. Intell. Inf. Syst. 2014, 3(2), 13-18. doi: 10.11648/j.ijiis.20140302.11
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Download@article{10.11648/j.ijiis.20140302.11,
author = {Ömer Faruk Ertuğrul},
title = {Adaptive Texture Energy Measure Method},
journal = {International Journal of Intelligent Information Systems},
volume = {3},
number = {2},
pages = {13-18},
doi = {10.11648/j.ijiis.20140302.11},
url = {https://doi.org/10.11648/j.ijiis.20140302.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20140302.11},
abstract = {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.},
year = {2014}
}
TY - JOUR T1 - Adaptive Texture Energy Measure Method AU - Ömer Faruk Ertuğrul Y1 - 2014/06/30 PY - 2014 N1 - https://doi.org/10.11648/j.ijiis.20140302.11 DO - 10.11648/j.ijiis.20140302.11 T2 - International Journal of Intelligent Information Systems JF - International Journal of Intelligent Information Systems JO - International Journal of Intelligent Information Systems SP - 13 EP - 18 PB - Science Publishing Group SN - 2328-7683 UR - https://doi.org/10.11648/j.ijiis.20140302.11 AB - 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. VL - 3 IS - 2 ER -
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