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Specific Color Detection in Images using RGB Modelling in MATLAB

by Vishesh Goel, Sahil Singhal, Tarun Jain, Silica Kole
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 161 - Number 8
Year of Publication: 2017
Authors: Vishesh Goel, Sahil Singhal, Tarun Jain, Silica Kole

Vishesh Goel, Sahil Singhal, Tarun Jain, Silica Kole . Specific Color Detection in Images using RGB Modelling in MATLAB. International Journal of Computer Applications. 161, 8 ( Mar 2017), 38-42. DOI=10.5120/ijca2017913254

@article{ 10.5120/ijca2017913254,
author = { Vishesh Goel, Sahil Singhal, Tarun Jain, Silica Kole },
title = { Specific Color Detection in Images using RGB Modelling in MATLAB },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 161 },
number = { 8 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 38-42 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2017913254 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:06:55.473077+05:30
%A Vishesh Goel
%A Sahil Singhal
%A Tarun Jain
%A Silica Kole
%T Specific Color Detection in Images using RGB Modelling in MATLAB
%J International Journal of Computer Applications
%@ 0975-8887
%V 161
%N 8
%P 38-42
%D 2017
%I Foundation of Computer Science (FCS), NY, USA

This paper gives an approach to recognize colors in a two-dimensional image using color thresh-holding technique in MATLAB with the help of RGB color model to detect a selected color by a user in an image. The methods involved for the detection of color in images are conversion of three dimensional RGB image into gray scale image and then subtracting the two images to get two dimensional black and white image, using median filter to filter out noisy pixels, using connected components labeling to detect connected regions in binary digital images and use of bounding box and its properties for calculating the metrics of each labeled region. Further the color of the pixels is recognized by analyzing the RGB values for each pixel present in the image. The algorithm is implemented using image processing toolbox in MATLAB. The results of this implementation can be used in security applications like spy robots, object tracking, segregation of objects based on their colors, intrusion detection.

  1. Rafael C. Gonzalez (University of Tennessee), Richard E. Woods (MedData Interactive) and Steven L. Eddins (The MathWorks, Inc.), in ‘Digital Image Processing Using MATLAB’ Second Edition,2009 by Gatesmark, LLC.
  2. Alasdair McAndrew, in ‘An Introduction to Digital Image Processing with Matlab, Notes for SCM2511 Image Processing1’, School of Computer Science and Mathematics, Victoria University of Technology.
  3. Digital image processing using Matlab -Gonzalez woods & Eddins
  4. R. S. Berns, “Principles of Color Technology” (3rd edition New York: Wiley, 2000)
  5. G. Wyszecki and W. S. Styles, “Color Science: Concepts and Methods, Quantitative Data and Formulae” (2nd edition New York: Wiley, 1982)
  6. J. L. Vincent, “Morphological Grayscale Reconstruction in Image Analysis: Applications and Efficient Algorithms”, IEEE Transactions on Image Processing, vol. 2, pp. 176–201, 1993.
  7. Devi, H.K.A., (2006). Thresholding: A Pixel-Level Image Processing Methodology Preprocessing Technique for an OCR System for the Brahmi Script. Ancient Asia. 1, pp.161–165.
  8. The Multi-stage Approach to Grey-Scale Image Thresholding for Specific Applications, Van Solihin and C. G. Leedham
  9. Document Image Analysis by Rangachar Kasturi, Louis Lam, Seong - Whan Lee & Ching Y. Suen.
  10. M. Sezgin and B. Sankur, “Survey over Image Thresholding Techniques and Quantitative Performance Evaluation”, Journal of Electronic Imaging, vol. 13, no. 1, pp. 146–168, Jan. 2004.
  11. N. R. Pal and D. Bhandari, “On Object Background Classification”, International Journal Syst. Science, vol. 23, no. 11, pp. 1903–1920, Nov. 1992.
  12. N. Otsu, “Threshold Selection Method from Gray-level Histograms”, IEEE Transactions on Systems, Man, Cybernetics, vol. SMC-9, no. 1, pp. 62–66, Jan. 1979.
  13. J. Kittler and J. Illingworth, “Minimum error Thresholding”, Pattern Recognition, vol. 19, no. 1, pp. 41–47, 1986.
  14. R. Guo and S. M. Pandit, “Automatic Threshold Selection based on Histogram Modes and a Discriminant Criterion”, Machine Vision Applications, vol. 10, no. 5–6, pp. 331–338, Apr. 1998.
  15. Wang, Zhou, and David Zhang. "Progressive switching median filter for the removal of impulse noise from highly corrupted images." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 46.1 (1999): 78-80.
  16. Brownrigg, D. R. K. "The weighted median filter." Communications of the ACM 27.8 (1984): 807-818.
  17. Loupas, T., W. N. McDicken, and P. L. Allan. "An adaptive weighted median filter for speckle suppression in medical ultrasonic images." IEEE transactions on Circuits and Systems 36.1 (1989): 129-135.
  18. Eng, How-Lung, and Kai-Kuang Ma. "Noise adaptive soft-switching median filter." IEEE Transactions on image processing 10.2 (2001): 242-251.
  19. Haralick, Robert M., and Linda G. Shapiro. "Image segmentation techniques." Computer vision, graphics, and image processing 29.1 (1985): 100-132.
  20. F. Meyer, “Color image segmentation”, Proceedings of 4th International Conference on Image Processing, pp. 523–548, 1992.
Index Terms

Computer Science
Information Sciences


MATLAB Image processing toolbox color detection RGB image Image segmentation Image filtering Bounding box.