Color detection in RGB-modeled images using MAT LAB

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    This research work introduces a method of using color thresholds to identify two-dimensional images in MATLAB using the RGB Color model to recognize the Color preferred by the user in the picture. Methodologies including image color detection convert a 3-D RGB Image into a Gray-scale Image, at that point subtract the two pictures to obtain a 2-D black-and-white picture, filtering the noise picture elements using a median filter, detecting with a connected component mark digital pictures in the connected area and utilize the bounding box and its properties to calculate the metric for every marking area. In addition, the shade of the picture element is identified by examining the RGB value of every picture element present in the picture. Color Detection algorithm is executed utilizing the MATLAB  Picture handling Toolkit. The result of this implementation can be used in as a bit of security applications such as spy robots, object tracking, Color-based object isolation, and intrusion detection.

     


  • Keywords


    MATLAB, picture handling tool kit, shading location, RGB picture, picture division, picture separating, bounding box.

  • References


      [1] Otsu N, “A threshold selection method from gray-level histograms”, IEEE transactions on systems, man, and cybernetics, Vol.9, No.1, (1979), pp.62-66.

      [2] Wyszecki G & Stiles WS, Color science, New York: Wiley, (1982).

      [3] Brownrigg DRK, “The weighted median filter”, Communications of the ACM, Vol.27, No.8,(1984), pp.807-818.

      [4] Kittler J & Illingworth J, “Minimum error thresholding”, Pattern recognition, Vol.19, No.1, (1986), pp.41-47.

      [5] Gonzalez RC & woods RE, Digital Image Processing, Addison-Wesely Publishing Company, (1992).

      [6] Pal NR. & Bhandari D, “On object background classification”, International journal of systems science, Vol.23, No.11, (1992), pp.1903-1920.

      [7] Vincent L, “Morphological grayscale reconstruction in image analysis: applications and efficient algorithms”, IEEE transactions on image processing, Vol.2, No.2, (1993), pp.176-201.

      [8] Solihin, Y & Leedham CG, “The multi-stage approach to grey-scale image thresholding for specific applications”, Nanyang Technological University, School of Computer Engineering, Nanyang Avenue, Republic of Singapore, (2000).

      [9] Berns, RS & Reiman DM, “Color managing the third edition of Billmeyer and Saltzman's Principles of Color Technology”, Color Research & Application, Vol.27, No.5,(2002), pp.360-373.

      [10] Gonzalez RC, Woods RE & Eddins S.L, Digital Image Processing Using MATLAB, Pearson Education, Inc, (2004).

      [11] McAndrew A, “An introduction to digital image processing with matlab notes for scm2511 image processing”, School of Computer Science and Mathematics, Victoria University of Technology, (2004).

      [12] Sezgin M & Sankur B, “Survey over image thresholding techniques and quantitative performance evaluation”, Journal of Electronic imaging, Vol.13, No.1,(2004), pp.146-166.

      [13] Abadpour A, Color Image Processing Using Principal, (2005).

      [14] Senthamaraikannan D, Shriram S & William J, “Real time color recognition”, International Journal of Innovative Research In Electrical, Electronics, Instrumentation And Control Engineering, Vol.2, No.3,(2014).

      [15] Rani NS, Neethu OP & Ponnath N, “Automatic Vehicle Tracking System Based on Fixed Thresholding and Histogram Based Edge Processing”, International Journal of Electrical and Computer Engineering, Vol.5, No.4,(2015).

      [16] Rani NS, Verma SK & Joseph A, “A Zone Based Approach for Classification and Recognition Of Telugu Handwritten Characters”, International Journal of Electrical and Computer Engineering, Vol.6, No.4,(2016).


 

View

Download

Article ID: 13391
 
DOI: 10.14419/ijet.v7i2.31.13391




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.