DIFFERENTIATING DEFECTS IN RED OAK LUMBER BY DISCRIMINANT ANALYSIS USING COLOR, SHAPE, AND DENSITY

Defect color, shape, and density measures aid in the differentiation of knots, bark pockets, stain/mineral streak, and clearwood in red oak, ( Quercus rubra ). Various color, shape, and density measures were extracted for defects present in color and X-ray images captured using a color line scan camera and an X-ray line scan detector. Analysis of variance was used to determine which color, shape, and density measures differed between defects. Discriminant classifiers were used to test which defect measures best discriminated between different defects in lumber. The ANOVA method of model measure selection was unable to provide a direct method of selecting the optimum combination of measures; however, it did provide insight as to which measure should be selected in cases of confusion between defects. No single sensor measure provided overall classification accuracy greater than 70%, indicating the need for multisensor and multimeasure information for defect classification. When used alone, color measures resulted in the highest overall defect classification accuracy (between 69 and 70%). Shape and density measures resulted in the lowest overall classification accuracy (between 32 and 53%); however, when used in combination with other measures, they contributed to a 5-10% increase in defect classification accuracy. It was determined that defect classification required multisensor information to obtain the highest accuracy. For classifying defects in red oak, sensor measures should include two color mean values and two standard deviation values, a shape measure, and a X-ray standard deviation value.

[1]  Daniel L. Schmoldt,et al.  Machine Vision Systems for Processing Hardwood Lumber and Logs , 1992 .

[2]  Olle Hagman,et al.  Multivariate image analysis methods to classify features on scots pine : evaluation of a multisensor approach , 1993 .

[3]  D. E. Kline,et al.  Machine Vision Technology for the Forest Products Industry , 1997, Computer.

[4]  Urs Buehlmann,et al.  Character-marked furniture: potential for lumber yield increase in rip-first rough mills , 1998 .

[5]  D. Wolf,et al.  Evaluation of colour spaces in computer vision application of wood defects detection , 1993, Proceedings of IEEE Systems Man and Cybernetics Conference - SMC.

[6]  Eric R. Ziegel,et al.  Applied Multivariate Data Analysis , 2002, Technometrics.

[7]  R. Lyman Ott.,et al.  An introduction to statistical methods and data analysis , 1977 .

[8]  David J. Hand,et al.  Discrimination and Classification , 1982 .

[9]  Daniel L. Schmoldt,et al.  A Multiple Sensor Machine Vision System for Automatic Hardwood Feature Detection , 1993 .

[10]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[11]  Azriel Rosenfeld,et al.  Computer Vision , 1988, Adv. Comput..

[12]  Par Dunias,et al.  Knowledge-based machine vision , 1994, Other Conferences.

[13]  C. J. Huberty,et al.  Applied Discriminant Analysis , 1994 .

[14]  B. Everitt,et al.  Applied Multivariate Data Analysis. , 1993 .

[15]  Richard W. Conners,et al.  A machine vision system forautomatically grading hardwood lumber , 1992 .

[16]  C. Heckler.  Applied Discriminant Analysis , 1995 .

[17]  Olli Silven,et al.  Development of color-vision-based solutions for lumber grading , 1995, Electronic Imaging.

[18]  Tai-Hoon Cho,et al.  A Knowledge-Based Machine Vision System for Automated Industrial Web Inspection , 2001, Int. J. Fuzzy Log. Intell. Syst..

[19]  Arthur R. Weeks.  Fundamentals of electronic image processing , 1996, SPIE/IEEE series on imaging science and engineering.

[20]  D. Earl,et al.  Machine Vision Technology the Forest for Products Industry , 1997 .

[21]  Wolfgang Poelzleitner,et al.  Real-time classification of wooden boards , 1991, Other Conferences.

[22]  Jouko Lampinen,et al.  Self-Organizing Feature Extraction in Recognition of Wood Surface Defects and Color Images , 1996, Int. J. Pattern Recognit. Artif. Intell..