Image Blocks Model for Improving Accuracy in Identification Systems of Wood Type

Image-based recognition systems commonly use an extracted image from the target object using texture analysis. However, some of the proposed and implemented recognition systems of wood types up to this time have not been achieving adequatue accuracy, efficiency and feasable execution speed with respect to practicality. This paper discussed a new method of image-based recognition system for wood type identification by dividing the wood image into several blocks, each of which is extracted using gray image and edge detection techniques. The wood feature analysis concentrates on three parameters entropy, standard deviation, and correlation. Our experiment results showed that our method can increase the recognition accuracy up to 95%, which is faster and better than the previous existing method with 85% recognition accuracy. Moreover, our method needs only to analyze three feature parameters compared to the previous existing method needs to analyze seven feature parameters, ang thus implying a simpler and faster recognition process. Keywords—image processing; pattern recognition; ANN; wood identification.