Automated optical grading of timber

Australia grows large quantities of radiata pine for domestic consumption and a significant proportion of this is graded to an Australian Standard Appearance Grade. This paper describes automating the visual inspection of this timber in order to speed processing and improve quality control of the product. The requirement is to detect and identify the visual features on the surface of the timber after the surface has been dressed. These features include sound and encased knots of various sizes pith bark bluestain holes and wane. The image is captured using a linear array CCD camera as the board moves underneath on a conveyor belt. The first stage is to detect the areas that contain features. The image is divided into smaller local areas and first and second order statistical measures are calculated. These form the input to a neural network that has been trained to classify the local areas into clear and feature areas. The choice of measures is crucial to the ability of the neural network to perform the classification of local areas. The second stage is to determine the type of feature in the feature local areas. Various methods are employed to determine a threshold that segments the feature correctly. The size of the feature can be used to identify it uniquely. The list of features and their positions forms the input to the grading program. The grading rules defmed

[1]  Yu-Chi Ho,et al.  On pattern classification algorithms introduction and survey , 1968 .

[2]  William S. Meisel,et al.  Computer-oriented approaches to pattern recognition , 1972 .

[3]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[4]  R. Haralick,et al.  Computer Classification of Reservoir Sandstones , 1973 .

[5]  Azriel Rosenfeld,et al.  A Comparative Study of Texture Measures for Terrain Classification , 1975, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  Richard W. Conners,et al.  A Theoretical Comparison of Texture Algorithms , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Robert M. Haralick,et al.  18 Image texture survey , 1982, Classification, Pattern Recognition and Reduction of Dimensionality.

[8]  Richard W. Conners,et al.  Identifying and Locating Surface Defects in Wood: Part of an Automated Lumber Processing System , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Luc Van Gool,et al.  Texture analysis Anno 1983 , 1985, Comput. Vis. Graph. Image Process..

[10]  Sehran Tatari,et al.  Automatic Recognition Of Defects In Wood , 1987, Other Conferences.

[11]  Ramesh C. Jain,et al.  Automatic Solder Joint Inspection , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Dominik Paul,et al.  VISTA: Visual Interpretation System for Technical Applications-Architecture and Use , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Robert M. Hodgson,et al.  Texture Measures for Carpet Wear Assessment , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  M.Sehran Tatari.  Selection And Parametrization Of Texture Analysis Methods For The Automation Of Industrial Visual Inspection Tasks , 1988, Optics & Photonics.

[15]  Peter J. M. Sobey.  The automated visual inspection and grading of timber , 1989 .

[16]  E. C. Semple,et al.  Detection and sizing visual features in wood using tonal measures and a classification algorithm , 1989, Pattern Recognit..