(untitled) (International Journal of Engineering and Computer Science, 2017)
The recognition of wood species is needed is many areas like construction industry, furniture manufacturing. Wood is traditionally classified by human experts. But human identification of wood type is not accurate and the manual identification is a time consuming process. So in this paper, an intelligent recognition for identification of wood species was developed. This paper uses image enhancement as a preprocessing techniques and uses a new method which divides the image into several blocks known as image blocking. Each block is extracted using gray image and edge detection techniques. In this paper, GLCM (gray-level co-occurrence matrix) is used as texture classification techniques. The GLCMs are generated to obtain three features: contrast, entropy and correlation. The classification technique used to classify the wood species is a correlation. Our experimental results showed that the proposed method can increase the recognition rate up to 95%, which is faster and better than the existing system which gives 85% recognition rate.