Comparative Study for Different Color Spaces of Image Segmentation Based on Prewitt Edge Detection Technique
DOI:
https://doi.org/10.32792/jeps.v10i1.45الكلمات المفتاحية:
Image Segmentation، Color Spaces، Prewitt Edge Detection، Structural Similarity Index Matrixالملخص
Image segmentation is one of the main step for images processing and analysis. It is the process ofrecognizing objects in images and may consist of two related processes, recognition and delineation. Edge
detection removes and reduces the quantity of unnecessary data and information and give its significant
information. In digital images, there are several representations of colors which have its own
characteristics. In this paper, the Prewitt edge detection was applied as segmentation technique of color
images in four different color spaces. These color spaces are RGB, HSV, YCbCr and YIQ respectively.
Structural similarity index matrix (SSIM), entropy and elapsed time are used as measures to compare
between the segmented images of the four color spaces. Experimental results showed that several
differences among segmented images for spaces by Prewitt technique. The RGB images have batter
resulst for SSIM, entropy and elapsed time values when comparing with YCbCr, HSV, and YIQ spaces.
Analysis of the obtained results of the RGB, HSV, YCbCr and YIQ color spaces are given in this paper.
المراجع
X. Wang, R. Hنnsch, L. Ma, and O. Hellwich, “Comparison of different color spaces for image
segmentation using graph-cut,” in International Conference on Computer Vision Theory and
Applications, 2014, vol. 1, pp. 301–308.
G. Hasting and A. Rubin, “Colour spaces-a review of historic and modern colour models,” African
Vision and Eye Health., vol. 71, no. 3, pp. 133–143, 2012.
C. Paper, J. Wu, Q. Shijie, and R. Zeng, “PCANet for Color Image Classification in Various Color
Spaces" International Conference on Cloud Computing and Security. Springer, Cham, June, 2017.
N. Dhanachandra and Y. J. Chanu, “A Survey on Image Segmentation Methods using Clustering
Techniques,” Asian Journal of Applied Science and Technology (AJAST), vol. 2, no. 1, pp.143-147, 2017.
M. Waseem Khan, “A Survey: Image Segmentation Techniques,” Asian Journal of Applied
Science and Technology (AJAST), vol. 3, no. 2, pp. 89–93, 2014.
[ [6] M. Raza, M. Sharif, M. Yasmin, S. Masood, and S. Mohsin, ” Research Journal of Applied
Sciences, Engineering and Technology., vol. 4, no. 18, pp. 3274–3282, 2012.
T.-F. Bronner, R. Boitard, M. T. Pourazad, P. Nasiopoulos, and T. Ebrahimi, “Evaluation of color
mapping algorithms in different color spaces,” International Society for Optics and Photonics,vol.3, pp.
-11, 2016.
G. G. Mary and M. S. Rani, “A Study on Secret Image Hiding in Diverse Color Spaces,”
International Journal of Advanced Research in Computer and Communication Engineering, vol. 5, no. 5,
pp. 779–783, 2016.
A. Trifan, A. J. R. Neves, and B. Cunha, “Evaluation of color spaces for user-supervised
color classification in robotic vision,” Rev. DO DATA, vol. 4, no. 2, pp. 1–6, 2004
K. Basha, P. Ganesan, V. Kalist, B. S. Sathish, and J. M. Mary, “Comparative Study of Skin Color
Detection and Segmentation in HSV and YCbCr Color Space,” Procedia Computer Science vol. 57, pp.
–48, 2015.
S. Sejpal, “Comparative Performance Analysis of Secured LWT- SVD Based Color Image
Watermarking Technique in YUV , YIQ and YCbCr Color Spaces,” International Journal of Computer
Applications,vol. 147, no. 7, pp. 34–40, 2016.
A. Semmo and D. Limberger, “Image Stylization by Oil Paint Filtering using Color Palettes Image
Stylization by Oil Paint Filtering using Color Palettes ,” J. Biomed. Heal. informatics, vol. 20, no. June,
pp. 615–623, 2015.
[١3] O. M. Can, Y. Ülgen, and A. Akın, “Use of the Color Spaces in Determining the Level of
Hemolysis in Blood Under Storage,” Springer . January, 2015.
H. Nejati, T. Do, and Y. Zhou, “Smartphone and Mobile Image Processing for Assisted Living,”
Signal Processing Magazine. `11July, 2016.
J. Zhu, T. Park, A. A. Efros, B. Ai, and U. C. Berkeley, “Unpaired Image-to-Image Translation
using Cycle-Consistent Adversarial Networks, International Conference on Computer Vision.” pp. 2223–
,2007.
K. Sirinukunwattana et al., “Gland Segmentation in Colon Histology Images : The GlaS Challenge
Contest,” pp. 1–28, 2016.
G. Larsson, M. Maire, and G. Shakhnarovich, “Learning Representations for Automatic
Colorization,”Springer ,Vol 3 pp. 5–7,2017.
M. Keuper, B. Andres, and T. Brox, “Motion Trajectory Segmentation via Minimum Cost
Multicuts,” International Conference on Computer Vision. 3271–3279, 2015.
A. Horé, “Image quality metrics : PSNR vs . SSIM Image quality metrics : PSNR vs . SSIM,”
International Conference on Pattern Recognition. August, 2010
التنزيلات
منشور
إصدار
القسم
الرخصة
The Authors understand that, the copyright of the articles shall be assigned to Journal of education for Pure Science (JEPS), University of Thi-Qar as publisher of the journal.
Copyright encompasses exclusive rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms and any other similar reproductions, as well as translations. The reproduction of any part of this journal, its storage in databases and its transmission by any form or media, such as electronic, electrostatic and mechanical copies, photocopies, recordings, magnetic media, etc. , will be allowed only with a written permission from Journal of education for Pure Science (JEPS), University of Thi-Qar.
Journal of education for Pure Science (JEPS), University of Thi-Qar, the Editors and the Advisory International Editorial Board make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in the Journal of education for Pure Science (JEPS), University of Thi-Qar are sole and exclusive responsibility of their respective authors and advertisers.
