Copyright © 2006 The Institute of Electronics, Information and Communication Engineers
Special Section on Machine Vision Applications -- Papers -- Image Inspection |
Visual Characterization of Paper Using Isomap and Local Binary Patterns
The authors are with Machine Vision Group, Department of Electrical and Information Engineering, University of Oulu, Finland. E-mail: dillian{at}ee.oulu.fi
In this paper, we study how a multidimensional local binary pattern (LBP) texture feature data can be visually explored and analyzed. The goal is to determine how true paper properties can be characterized with local texture features from visible light images. We utilize isometric feature mapping (Isomap) for the LBP texture feature data and perform non-linear dimensionality reduction for the data. These 2D projections are then visualized with original images to study data properties. Visualization is utilized in the manner of selecting texture models for unlabeled data and analyzing feature performance when building a training set for a classifier. The approach is experimented on with simulated image data illustrating different paper properties and on-line transilluminated paper images taken from a running paper web in the paper mill. The simulated image set is used to acquire quantitative figures on the performance while the analysis of real-world data is an example of semi-supervised learning.
Key Words: texture analysis, visualization, paper inspection
Manuscript received November 1, 2005. Manuscript revised January 18, 2006.
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