Specific Color Detection in Images using RGB Modelling in MATLAB

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]  G. Wyszecki,et al.  Color Science Concepts and Methods , 1982 .

[2]  H. Devi,et al.  Thresholding: A Pixel-Level Image Processing Methodology Preprocessing Technique for an OCR System for the Brahmi Script , 2006 .

[3]  D. R. K. Brownrigg,et al.  The weighted median filter , 1984, CACM.

[4]  Josef Kittler,et al.  Minimum error thresholding , 1986, Pattern Recognit..

[5]  Kannan,et al.  ON IMAGE SEGMENTATION TECHNIQUES , 2022 .

[6]  Kai-Kuang Ma,et al.  Noise adaptive soft-switching median filter , 2001, IEEE Trans. Image Process..

[7]  S. M. Pandit,et al.  Automatic threshold selection based on histogram modes and a discriminant criterion , 1998, Machine Vision and Applications.

[8]  F. Billmeyer,et al.  Principles of color technology , 1967 .

[9]  Y. Solihin,et al.  The Multi-stage Approach to GreyScale Image Thresholding for Specific Applications , 2002 .

[10]  T. Loupas,et al.  An adaptive weighted median filter for speckle suppression in medical ultrasonic images , 1989 .

[11]  N. Pal,et al.  On object background classification , 1992 .

[12]  VincentL..  Morphological grayscale reconstruction in image analysis , 1993 .

[13]  Suyash P. Awate,et al.  Computer Vision, Graphics, and Image Processing , 2016, Lecture Notes in Computer Science.

[14]  Luc Vincent,et al.  Morphological grayscale reconstruction in image analysis: applications and efficient algorithms , 1993, IEEE Trans. Image Process..

[15]  Zhou Wang,et al.  Progressive switching median filter for the removal of impulse noise from highly corrupted images , 1999 .

[16]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[17]  Lawrence O'Gorman,et al.  Document Image Analysis , 1996 .

[18]  B. S. Manjunath,et al.  Color image segmentation , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[19]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[20]  Wayne Nilback.  An introduction to digital image processing , 1985 .