Call for Paper - January 2022 Edition
IJCA solicits original research papers for the January 2022 Edition. Last date of manuscript submission is December 20, 2021. Read More

Experiencing Various Color Models on Colored Images

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Noor A. Ibraheem, Mokhtar M. Hasan
10.5120/ijca2017914608

Noor A Ibraheem and Mokhtar M Hasan. Experiencing Various Color Models on Colored Images. International Journal of Computer Applications 169(2):29-33, July 2017. BibTeX

@article{10.5120/ijca2017914608,
	author = {Noor A. Ibraheem and Mokhtar M. Hasan},
	title = {Experiencing Various Color Models on Colored Images},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2017},
	volume = {169},
	number = {2},
	month = {Jul},
	year = {2017},
	issn = {0975-8887},
	pages = {29-33},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume169/number2/27959-2017914608},
	doi = {10.5120/ijca2017914608},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Colors are important for human communicating with the daily encountered objects as well as his species, these colors should be represented formally and numerically within a mathematical formula so it can be projected on device/ computer storage and applications, this mathematical representation is known as color model that can hold the color space, by the means of color’s primary components (Red, Green, and Blue) the computer can visualizes what the human does in hue and lightness. In this work a review of most popular color models are given (which are RGB, CMY, HSV, and YCbCr) with the explanation of the components, color system, and transformation formula for each other, application areas and usages are also included. Comparison between these different color models is performed by applying Signal to noise Ratio (SNR) metric to indicate the best color models. Results analysis shows the RGB has better results according to SNR measure.

References

  1. K.N. Plataniotis, and A.N. Venetsanopoulos, “Color Image Processing and Applications”, book, Springer-Verlag New York, Inc. New York, NY, USA, ISBN:3-540-66953-1, 2000.
  2. Gonzalez and Wood Addison, "Digital image processing", Wesley, 1st edition, 1992.
  3. Haneet Kour,"Analysis on Image Color Model", International Journal of Advanced Research in computer and Communication Engineering, Vol.4, issue 12, 2015
  4. Mokhtar M. Hasan, Noor Adnan Ibraheem, “Mixture of GMMs and Mixture of Multiple Histograms for Image Segmentation: A Review”, International Journal of Computer Science, vol. 4 , issue 2, number 2, pp.739-743, July 2016.
  5. Rafiqul Z. Khan,Noor A. Ibraheem, “Novel Segmentation Algorithm based on Mixture of Multiple Histograms”, International Journal of Scientific & Engineering Research (IJSER), (ISSN 2229-5518), Volume 4 (8), pp. 1074-1087, France, August2013.
  6. Noor A. Ibraheem, Mokhtar M. Hasan, Rafiqul Z. Khan, Pramod K. Mishra, “Understanding Color Models: A Review”, ARPN Journal of Science and Technology (ISSN: 2225-7217) - (An International Journal), Volume 2 (3), April 2012.
  7. RafiqulZaman Khan, Noor Adnan Ibraheem, “Segmentation Algorithms for Vision Based HCI”, Proceedings of the Third Kuwait Conference on e-Services and e-Systems (KCESS-2012), ACM Digital Library, New York, , held at Kuwait University, December 18-20, 2012.
  8. Noor A. Ibraheem, Rafiqul Z. Khan, “Multiple Histogram Technique for Robust Skin Color Based Segmentation”, American Journal of Engineering Research (AJER), Volume 2(5), (e-ISSN 2229-5518), (p-ISSN 2320-0936), pp. 50-54, USA, May 2013.
  9. Mokhtar M. Hasan, Noor Adnan Ibraheem, “Melting of Multiple GMM and Multiple Histogram in Segmentation, Gesture Recognition”, International Journal of Computer Systems, vol. 3 (7):517-520, India, July, 2016.
  10. Webpage: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio.
  11. Fatin E. M. Al-Obaidi, “Image Quality Assessment for Defocused Blur Images”, American Journal of Signal Processing, Vol. 5(3), pp. 51-55, 2015. Doi: 10.5923/j.ajsp.20150503.01
  12. Mokhtar M. Hasan, Pramod K. Mishra, “Superior Skin Color Model using Multiple of Gaussian Mixture Model”, British Journal of Science, vol. 6(1): 1-14, UK, July 2012.
  13. Mokhtar M. Hasan, Ahmed A. Abdul Redha, “Applying Quran Security and Hamming Codes for Preventing of Text Modification”, Baghdad Science Journal, University of Baghdad, Iraq, vol.8 (2): 408-418, June 2011.
  14. Mokhtar M Hasan, “New Rotation Invariance Features Based on Circle Partitioning”, Journal of Computer Engineering & Information Technology, vol. 2 (2), USA, July 2013, doi: 10.4172/2324-9307.1000108.
  15. Hasan, M.M., Mishra, P.K., “Direction Analysis Algorithm using Statistical Approaches”, SPIE 4th International Conference on Digital Image Processing, 8334-28, 83340L (2012), Malaysia, April 2012, doi: 10.1117/12.946046.
  16. Mokhtar M. Hasan, Pramod K. Mishra, “Novel Algorithm for Skin Color Based Segmentation using Mixture of GMMs”, Signal & Image Processing : An International Journal (SIPIJ), vol. 4 (4): 139-148, India, August 2013, doi: 10.5121/sipij.2013.4412
  17. Mokhtar M. Hasan, Pramod K. Mishra, “Comparative Study for Construction of Gesture Recognition System”, International Journal of Computer Science and Software Technology, vol. 4(1): 15-21, January-June 2011.
  18. Mokhtar M. Hasan and Pramod K. Mishra, “Robust Gesture Recognition Using Gaussian Distribution for Features Fitting”, International Journal of Machine Learning and Computing, IACSIT Organization, vol. 2(3):266-273, Singapore, June 2012.
  19. Mokhtar M. Hasan, “Object Filling Using Table Based Boundary Tracking”, Journal of College of Education for Women, vol. 28(1): 323-331, March 2017.
  20. RafiqulZaman Khan, Noor Adnan Ibraheem ,"Genetic Shape Fitting for Hand Gesture Modeling and Feature Extraction using Variable Length Chromosome", British Journal of Science, (ISSN 2047-3745), Volume 10 (1), UK, December 2013.
  21. Rafiqul Zaman Khan, Noor Adnan Ibraheem ,"Comparative Study and Analysis for Gesture Recognition Methodologies", International Journal of Advance Research in Science and Engineering, (e-ISSN 2319-8354), (p-ISSN 2319-8346), Volume 2(11), pp.64-69, India, November 2013.

Keywords

Color Model, RGB, CMY, HSV, YCbCr, skin color detection, segmentation.