The proposed method is demonstrated by samples of base paper. Finding defects on a base paper is not an easy task. There exist many different kinds of defects, for example holes (clean hole, wire hole etc.), spots (dirt spot, light spot, water spot etc.), wrinkles, and streaks. An uneven formation of paper makes the task more difficult. Defects in base paper may cause the paper web to break when coating material is applied to paper by a coating blade. Web breaks mean losing of production time. Therefore, those defects which are risks to runnability of paper, should be detected and classified reliably. The detection principle of contemporary surface inspection systems is mostly based on classifying defects in holes and spots, and measuring their sizes. The proposed method makes it possible to extract features which also describe the internal structure of defects. This kind of classifying could be useful also for inspection of other web products, e.g. board, nonwoven, and glassfiber.
The size of the base paper images was 512x512 and they were quantized to 32 gray levels (Figure 3). The feature vector consisted of 6 features, namely Energy, Contrast, and Mean, which are calculated from two co-occurrence matrices formed at different displacements, (2,0) and (4,0). The different displacements were used to avoid the problem concerning improper spatial resolution [14]. The window size used was 15x5. The size of the map was 9x9 units. The size of the training set was 100000 feature vectors extracted from faulty-free samples. The confidence level d was 99%.
Figure 3: (a) Base paper image and (b) its histogram.
In Figures 4(a)-(b) are the segmentation results with the proposed method with different values of T. Defects are marked with black color. The best result is obtained when using T=1. If a complete match is demanded for a faulty-free sample (T=0), a noisy segmentation is obtained. In Figures 4(c)-(d) are the segmentation results when using gray level thresholding. In Figure 4(c) an iterative optimal threshold algorithm was used [11](pages 118-119). The obtained threshold value was G=12. In Figure 4(d) the threshold was selected by hand according to the histogram in Figure 3(b) (G=9). The pixels having gray level value 32 were also classified to a defect in Figures 4(c)-(d). However, even if the thresholded image is quite good, thresholding is not a very applicable technique in this kind of problem. For discussion of different thresholding techniques and their restrictions, see for example [7], pages 14-28.
Figure 4: Base paper image after segmentation.
Defects are marked with black color.
(a) The proposed method (T=1).
(b) The proposed method (T=0).
(c) Gray level thresholding by an iterative algorithm (G=12).
(d) Gray level thresholding by hand (G=9).