Abstract
In this research, a neural network using backpropagation (BPNN) algorithm was trained and learned to work as the cone cells in human eyes to recognize the three fundamental cells’ colors and hues, as the neural network showed good results in training and testing the color feature it was trained and learned again to recognize two nature scenes images ; Red sunset and Blue sky images where both scenes images contain color interaction and different hues such as red-orange and blue-violet. The recognition process was based on color histogram technique in colored images which is a representation of the distribution of colors in an image by counting the number of pixels that have colors in each of a fixed list of color ranges, that span the image's color space , all possible colors in the image. The importance of this research is based on developing the ability of (BPNN) in images ‘objects recognition based on color feature that is very important feature in artificial intelligence and colored image processing fields from developing the systems of alarms robots in fire recognition , medical digenesis of tumors, certain pattern’s recognition in different segments of an image , face and eyes’ iris recognition as a part of security systems , it helps solve the problem of limitation of recognition process in neural networks in many fields.
Highlights
Colors and hues, as the neural network showed good results in training and testing the color feature it was trained and learned again to recognize two nature scenes images ; Red sunset and
The number of the pixels that are selected can be as much as the colored area for the selected color in the image; Selecting many pixels will increase the size of the array for each color,selecting few pixels will affect the recognition process accuracy of the (BPNN) while selecting too many pixels will affect the recognition process speed by taking more time to finish processing so in the work (50) pixels were selected for each color the red,green and blue which means each color component in the pixel matrix dimension is (50 x 1)
Using the extracted colors features from several images samples to train and test the Back Propagation Neural Network (BPNN) to function as the human eyes cone cells to recognize the three color samples, red, green and blue
Summary
Color can be thought of as a psychological and physiological response to light waves of a specific frequency or set of frequencies impinging upon the eye. While the rods on the retina are sensitive to the intensity of light, they cannot distinguish between light of different wavelengths. When light of a given wavelength enters the eye and strikes the cones of the retina, a chemical reaction is activated which result in an electrical impulses being sent along nerves to the brain. It is believed that there are three kinds of cones, each sensitive to its own range of wavelengths within the visible light spectrum. The green cone can be activated by wavelengths of lights associated with the colors yellow and blue [1]. (R2008a) [4], a program was written to perform the operation of a true color image processing to extract the color feature that is needed. The displayed true color images were unenhanced images as shown in Figure (2) ,it may show difference in colors interactions and difference in contrast levels and brightness as well so the images needed to be enhanced by using color histogram technique
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