Development of a color machine vision method for wood surface inspection
|Organizations:||University of Oulu, Faculty of Technology, Department of Electrical Engineering
|Online Access:||PDF Full Text (PDF, 14.4 MB)|
|Persistent link:|| http://urn.fi/urn:isbn:9514254244
|Publish Date:|| 1999-11-03
|Thesis type:||Doctoral Dissertation
|Defence Note:||Academic Dissertation to be presented with the assent of the Faculty of Technology, University of Oulu, for public discussion in Raahensali (Auditorium L 10), Linnanmaa, on November 26th, 1999, at 12 noon.
Professor Jouko Lampinen
Professor Arto Usenius
The purpose of this thesis is to present a case study of the development, implementation and performance analysis of a color-based visual surface inspection method for wood properties. The main contribution of the study is to answer the need of design strategies, performance characterization methods and case studies in the field of automated visual inspection, and especially wood surface inspection.
In real time color-based inspection, the complexity of the methods is important. In this study, defect detection and recognition methods based on color histogram percentile features are proposed. The color histogram percentile features were noticed to be able to recognize well wood surface defects with relatively low complexity.
A common problem in visual inspection applications is the collection and labelling of training material since human made labellings can be errorneous. Further, the classifiers are relatively static when once trained, thus offering only little possibilities for adjusting classification. In the study, a self-organizing map (SOM) -based approach for classifier user interface in visual surface inspection problems is introduced. The approach relieves the labelling of training material, simplifies retraining, provides an illustrative an intuitive user interface and offers a convenient way of controlling classification.
The study is illustrated with four experiments related to the method development and analysis. In the first experiment, a simulator environment is used for determining the relationship of the defect detection and recognition and grading accuracy. The second experiment considers the suitability of different color spaces for wood defect recognition under changing illumination. RGB color space gives the best results compared to grey-level and other color spaces. The third experiment presents the experimental wood surface inspection setup implementing the method developed in this study. Comparative performance analysis results are presented and the difficulties, mainly caused by segmentation of the defects, are discussed. The fourth experiment demonstrates the suitability of the method for parquet sorting and shows the potential of the non-segmenting approach.
Acta Universitatis Ouluensis. C, Technica
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