Defect identification of lumber through correlation technique with statistical and textural feature extraction method

Feature extraction is an important component of a pattern recognition system. A well-defined feature extraction algorithm makes the identification process more effective and efficient. Several techniques exist for the quality checking of wooden materials. However, image based quality checking of wooden materials still remains a challenging task. Although trivial quality checking methods are available, they do not give useful results in most situations. This paper addresses the issue of quality checking of wooden materials using statistical and textural feature extraction techniques with high accuracy and reliability. In our work, a wood defect identification system has been designed based on pre-processing techniques, feature extraction and by correlating the features of those wood species for their classification. The most popular technique used for the textural classification is Gray-level Co-occurrence Matrices (GLCM). The features from the enhanced images are thus extracted using the GLCM is correlated, which determines the classification between the various wood species. Experiments conducted under the proposed conditions showing significant results are presented.