Color Based Segmentation Towards Structural Distribution of Image Data

This paper analyses the digital image acquired in real time using color based segmentation to estimate the structural distributions of image data. Structural distribution using computer systems has become a major field of interest. Furthermore, distribution of data in a digital image with different colors has gained much importance in the last decade due to its wide applications. An image is acquired in real time through image acquisition toolbox and is exported to MATLAB workspace. Color based image segmentation has been explored and implemented to locate different colored structures in an acquired image. This is followed by the plotting of corresponding histograms of individual red, green and blue planes, respectively to indicate brightness at each point that in turn, represents the pixel count. Finally, the segmented pixels are classified using the Nearest Neighbor rule. It is observed that the designed algorithm possesses the capability to determine the structural distribution of input image data. The results suggest that the methodology adopted can further be used for brake light detection system, to locate different colored objects in satellite images, for authentication of paper currency, in fashion industry etc.