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GLCM Based Parallel Texture Segmentation using A Multicore Processor
This paper investigates the using of Gray Level Co-Occurrence Matrix (GLCM) based on supervised texture
segmentation. In most texture segmentation methods, the processing algorithm is applied to a window of the original image
rather than to the entire image using sliding scheme. To attain a good segmentation accuracy especially in the boundaries,
optimal size of window is determined, or windows of variant sizes are used. Both options are very time consuming. Here, a new
technique is proposed to build an efficient GLCM based texture segmentation system. This scheme uses a fixed window of
variant apertures. This will reduce the computation overhead and recourses that required to compute GLCM, and will improve
the segmentation accuracy. Image's windows are multiplied with a matrix of local operators. After that, GLCM is computed
and features are extracted and classified and the segmented image is produced. In order to reduce the segmentation time, two
similarity metrics are used to classify the texture pixels. Euclidean metric is used to find the distance between the current and
previous GLCM. If it is above a predefined threshold, then the computation of GLCM descriptors are required. Gaussian
metric is used as a distance measure between two GLCM descriptors. Furthermore, a median filter is applied to the segmented
image. Finally, the transition and misclassified regions are refined. The proposed system is parallelized and implemented on a
multicore processor.
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[19] (right). 0 100 200 300 # core time (sec) parfor 207 128 110 110 121 125 spmd 207 110 80 78 98 112 OpenMp 64 35 25 22 18 18 1 2 3 4 5 6 0 20 40 60 threshold value E% 0 0.031 0.264 1.272 4.688 time reduction% 0 7 17 33 47 th 1 th 2 th 3 th 4 th 5 Figure 5. Segmentation results with comparison to previous works. 16 The International Arab Journal of Information Technology, Vol. 16, No. 1, January 2019 http://www.realworldtech.com/ page.cfm? ArticleID=RWT040208182719, Last Visited, 2008.
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[19] Zheng Y. and Chen K., “A Hierarchical Algorithm for Multiphase Texture Image Segmentation,” ISRN Signal Processing, vol. 2012, pp. 1-11, 2012. Shefa Dawwd was born in Mosul- Iraq in 1970. He received the B.Sc degree in electronic and communication Engineering, the M.Sc and the Ph.D degree in computer Engineering in 1991, 2000, and 2006, respectively. He is presently a faculty member (Associate Professor) in the computer engineering department/University of Mosul. His main research interests include image & signal processing and their hardware models, parallel computer architecture, hardware implementation and GPU based systems. He has authored more than 30 research papers and book chapters. He has been an editorial member of several national and international journals.