Backpropagation Neural Network For Colour Recognition

Colour Image Processing (CIP) is useful for inspection system and Automatic Packing Lines Systems. CIP usually needs expensive and special hardware as well as software to extract colour from image. Most of CIP software use statistical methods to extract colours and some system use Neural Network such as Counter-Propagation and Back-Propagation . Some researchers had used Neural Network methods to recognize colour of Commission Internationale de L'Ec1airage (CIE) Models either L *u *v or L *a *b. CIE colour components need special and expensive devices to extract their values from an image. However, this project will use RED, GREEN, BLUE (RGB) colour components, which can be read from an image. In this research, RGB values are used to represent the colour. RGB values are used in two forms. The first form is the actual values that are used in PPM File Format within (0,255) and the second form is normalized RGB values within (0, I ). Back-Propagation Neural Network is used to recognize colour in RGB values. It is found that RGB is useful when used with Neural Network and the Normalized RGB value is faster in the learning of neural network.

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