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2017 (Vol. 14, Issue: 9)
Research Article

Classification Archaeological Fragments into Groups

1Nada A. Rasheed, 2Md Jan Nordin, 1Awfa Hasan Dakheel, 1Wessam Lahmod Nados and 1Maysoon Khazaal Abbas Maaroof
1University of Babylon, Hillah, 51001, Iraq
2Universiti Kebangsaan Malaysia (UKM), Selangor, 43600, Malaysia

DOI: 10.19026/rjaset.14.5072
Submitted Accepted Published
April 12, 2017 June 23, 2017 September 15, 2017

  How to Cite this Article:

1Nada A. Rasheed, 2Md Jan Nordin, 1Awfa Hasan Dakheel, 1Wessam Lahmod Nados and 1Maysoon Khazaal Abbas Maaroof, 2017. Classification Archaeological Fragments into Groups.  Research Journal of Applied Sciences, Engineering and Technology, 14(9): 324-333.

DOI: 10.19026/rjaset.14.5072

URL: http://www.maxwellsci.com/jp/mspabstract.php?jid=RJASET&doi=rjaset.14.5072


The objective of this study is to suggest a method for classifying archeological fragments into groups. For this task, the method suggested begins with conversion of images from their original RGB color to a Hue, Saturation and Value (HSV) color. From that point forward, a 2D median filtering algorithm is implemented to remove any resultant noise. Next, each image is separated into six sub-block of equivalent size. In order to extract the feature for each sub-block, it is represented as a vector intersection of colors between each part of the image and the corresponding parts of the five remaining images. At this stage, we obtain a vector that consists of the six values for each image. For the last stage, a Self-Organization Map (SOM) Neural Network classifies the fragments into groups relying upon their HSV color feature. The algorithm was tested on several images of pottery fragments and the results achieved demonstrate this approach is promising and is able to cluster fragments into groups with high precision.

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    Competing interests

The authors have no competing interests.
    Open Access Policy

This article is 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, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.


© The Author(s) 2017

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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