Defect Identification of Lumber Through Correlation Technique with Statistical Feature Extraction Method

extraction is an important component of a pattern recognition system. A well-defined feature extraction algorithm makes the identification process more effective and efficient. Quality checking is one of the most prominent steps in many applications using Feature extraction. 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 feature extraction techniques with high accuracy and reliability. Experiments conducted under the proposed conditions showing significant results are presented. Quality checking has been a focus of investigation over last decades. Material quality checking plays a vital role in product based industries. Most of the literature concentrates on quality checking of material based on dynamic motion of image capturing. This paper presents a noval approach for quality checking of wooden material based on correlation Coefficient based feature extraction technique. Pasquale Flauto and Salvatore Musella (1) has proposed a technique and quality control methods used during the development phases of an expert system prototype, known as Hydronet for conventional (procedural)software systems used by most informatics leading companies. Junfeng Li and Wenzhan Dai al.(2) has proposed the algorithm that makes full use of perfect integral comparison mechanism of the correlation coefficient and the well matching of discrete wavelet transform with multichannel model of human visual system. The algorithm can not only evaluate the integral and detail quality of image fidelity accurately but also bears more consistency with the human visual system then the traditional method PSNR. Christof Knoess et al.(3) has presented their work on daily quality checking of the natural background radioactivity from the new scintillator material LSO as a uniform source. Liwei Wang et al (4) has proposed that the matrices based 2-D algorithms are equivalent To special cases of image block based feature extraction. This method reduces the computational effort and the possibility of singularity in feature extraction Chulhee Lee and Euisun Choi (5) has proposed a method to optimize feature extraction for multiclass problems. The algorithm consistently provides better performances compared with the conventional feature extraction algorithms. Yong- zhi Li et al (6) has proposed the method with more powerful capability to eliminate the statistical correlation between features and improve efficiency of feature extraction. This is better method than original KMMC and kernel principal component analysis (KPCA) in terms of efficiency and stability about feature extraction. Andreas Hanemann and Martin Sailer .(7) has presented a framework for the new kind of event correlation which is called service-oriented event correlation. This bridges the gap between the management of the infrastructure and the offer of services for the customers with respect to the service fault diagnosis. The organization of the paper is as follows: section 2 describes statistical features extracted from image section 3 focuses on outline of the approach, section 4 deals on experiment and results, section 5 deals with discussion and section 6 concludes with conclusion.