Published Paper Details:

Wood is that the thin, fibrous tissue found in the tops and roots of trees and other woody plants. It has been used for thousands of years for both fuels and as a construction material. Natural resources such as wood have become scarce and costly. Increasing consumption and reducing rejection (loss) is a major challenge in the timber industry. Wood defects are due to physical activity, genetics, or environmental influences during adolescence. These defects will reduce the amount of wood consumption. However, it is very difficult to determine if there is a defect exists, and the level of defects. Therefore, the successful detection of wood deformities is very important. A new wood defect detection method an efficient algorithm for wood defect identification using a neural classifier was proposed in this research for the detection of the wood defect. This chosen work purposes the tasks of extracting, classifying, and segmenting the five types of wood defect images using more efficient supervised learning approaches for more accurate and computationally efficient segmentation. The main aim of the purposed work to develop a computer-aided classification system of wood defects.