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Multivariate Imaging for Automated Process Control in the Agro Industry
2.3 Unsupervised classification
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In unsupervised classification, patterns or clusters can be detected on the basis of their latent spectral correlation only. The parameters of the pattern recognition algorithm can be fixed . The fixed parameters are further referred to as the pattern recogni tion model. The model can then be used to classify unknown multivariate images. Two unsupervised classification techniques have been implemented: a modified fuzzy C-means (FCM) algorithm (Noordam et al. 2000), and a Kohonen artificial neural network (Kohonen 1984). An example of the latter is shown in figure 3.
2.4 Supervised classification
When pre-information is available, supervised classification can be applied. In this case, patterns or clusters derived from multivariate images are assigned a class on forehand. This means that examples (pixels) of these classes have to be segmented from existing multivariate images (training set). Then, a classification model can be calculated that includes this information. Examples of implemented supervised classification techniques are linear discriminant analysis (LDA) and artificial neural networks (multilayer feedforward or MLF) (van den Broek 1997). The most difficult step in supervised classification is the selection of training set examples and the assignment of their classes. There are several ways to do this: pixel based selection, region based selection, principal component analysis (PCA) selection, multivariate magic wand or unsupervised clustering. The first two are performed by either mouse clicking or mouse region selection. Selection by PCA is shown in figure 4. An original multivariate image is decomposed into latent images by PCA. When two of the latent images are plotted (score plot), an overview is obtained of the inherent pixel correlation. Possible clusters can be extracted with mouse interaction. The multivariate magic wand is a multivariate implementation of the classical magic wand. Segmentation with unsupervised clustering techniques is described earlier is this article.
Fig. 3. Original image (upper) is clustered unsupervised with Kohonen clustering (middle) and one detected feature is back projected (lower) in nearby wavelengths (low spectral resolution) . As a result of this, standard color systems discriminate poorly between similar colored objects. Multivariate imaging system can overcome this problem and are able to detect similar colored defects in a specific wavelength range. In the following example, a multivariate imaging system (Imspector) is used to capture images in the visible wavelength range from 430 - 900 nm with 5 nm steps. Figure 5 shows a piece of minced meat. The multivariate stack of 96 images is combined
3. RESULTS 3.1 Multivariate imaging in practice
Standard color imaging systems are often used to monitor and inspect the quality of different meat products during the production and processing of meat. However, standard col or cameras integrate over a large range of wavelengths and therefore can not detect abrupt intensity changes 305
CPRO in Wageningen for the use of his imaging equipment.
6. REFERENCES Geladi, Paul and Hans Grahn (1996). Multivariate image analysis. John Wiley & Sons Ltd. Kohonen, T (1984) . Self-Organization and Associative Memory. Springer Verlag. Noordam, J .C., W.H.A.M. van den Broek and L.M.C. Buydens (2000). Geometrically guided fuzzy c-means clustering for multivariate image segmentation. In: 15th International Conference on Pattem Recognition (accepted). Barcelona, Spain. van den Broek, W .H.A.M. (1997). Chemometrics in spectroscopic near infrared imaging for plastic material recognition. PhD thesis. Catholic University of Nijmegen.
Fig. 8. Segmented image of minced meat with plastic fragments. The plastic fragments are correctly identified respectively figures 4 and 7 indicate that discrimination between the fat particles and the plastic fragments is possible. For the separation between these two classes linear combinations of all image planes have been used. To demonstrate the possibility of automated identification of (plastic) impurities in meat processing, a classification model is calculated. First, training pixels are selected from the stack of images as described in the previous paragraph. The selected pixels are labeled in 4 classes: plastic, fat, dark colored meat and light colored meat. These pixels are used to calculate the classification model. Linear Discriminant Analysis (LDA) is selected as supervised classification technique. The result of the LDA classification is shown in figure 8. The plastic fragments are correctly identified and discriminated from the fat particles. This example clearly shows that automated identification of impurities, which are transparent or difficult to see in the visible wavelength range, can be detected with multivariate imaging systems in the visi ble and near infrared region.
4. CONCLUSION The availability of exploration and classification tools for multivariate imaging allows maximum extraction of physicochemical and spatial information. The resulting classification models can be applied in agrotechnological process inspection and control. Experiments indicate that multivariate imaging systems have a surplus value above traditional vision systems.
5. ACKNOWLEDGEMENT The authors acknowledge the contribution of G. Otten for the implementation of the neural networks, R. van Soest and F. Golbach for the implementation of the magic wand and G. Polder from 307
!~ 0 riginallmage
1I!!I(i] 13
In unsupervised classification, patterns or clusters can be detected on the basis of their latent spectral correlation only. The parameters of the pattern recognition algorithm can be fixed . The fixed parameters are further referred to as the pattern recogni tion model. The model can then be used to classify unknown multivariate images. Two unsupervised classification techniques have been implemented: a modified fuzzy C-means (FCM) algorithm (Noordam et al. 2000), and a Kohonen artificial neural network (Kohonen 1984). An example of the latter is shown in figure 3.
2.4 Supervised classification
When pre-information is available, supervised classification can be applied. In this case, patterns or clusters derived from multivariate images are assigned a class on forehand. This means that examples (pixels) of these classes have to be segmented from existing multivariate images (training set). Then, a classification model can be calculated that includes this information. Examples of implemented supervised classification techniques are linear discriminant analysis (LDA) and artificial neural networks (multilayer feedforward or MLF) (van den Broek 1997). The most difficult step in supervised classification is the selection of training set examples and the assignment of their classes. There are several ways to do this: pixel based selection, region based selection, principal component analysis (PCA) selection, multivariate magic wand or unsupervised clustering. The first two are performed by either mouse clicking or mouse region selection. Selection by PCA is shown in figure 4. An original multivariate image is decomposed into latent images by PCA. When two of the latent images are plotted (score plot), an overview is obtained of the inherent pixel correlation. Possible clusters can be extracted with mouse interaction. The multivariate magic wand is a multivariate implementation of the classical magic wand. Segmentation with unsupervised clustering techniques is described earlier is this article.
Fig. 3. Original image (upper) is clustered unsupervised with Kohonen clustering (middle) and one detected feature is back projected (lower) in nearby wavelengths (low spectral resolution) . As a result of this, standard color systems discriminate poorly between similar colored objects. Multivariate imaging system can overcome this problem and are able to detect similar colored defects in a specific wavelength range. In the following example, a multivariate imaging system (Imspector) is used to capture images in the visible wavelength range from 430 - 900 nm with 5 nm steps. Figure 5 shows a piece of minced meat. The multivariate stack of 96 images is combined
3. RESULTS 3.1 Multivariate imaging in practice
Standard color imaging systems are often used to monitor and inspect the quality of different meat products during the production and processing of meat. However, standard col or cameras integrate over a large range of wavelengths and therefore can not detect abrupt intensity changes 305
CPRO in Wageningen for the use of his imaging equipment.
6. REFERENCES Geladi, Paul and Hans Grahn (1996). Multivariate image analysis. John Wiley & Sons Ltd. Kohonen, T (1984) . Self-Organization and Associative Memory. Springer Verlag. Noordam, J .C., W.H.A.M. van den Broek and L.M.C. Buydens (2000). Geometrically guided fuzzy c-means clustering for multivariate image segmentation. In: 15th International Conference on Pattem Recognition (accepted). Barcelona, Spain. van den Broek, W .H.A.M. (1997). Chemometrics in spectroscopic near infrared imaging for plastic material recognition. PhD thesis. Catholic University of Nijmegen.
Fig. 8. Segmented image of minced meat with plastic fragments. The plastic fragments are correctly identified respectively figures 4 and 7 indicate that discrimination between the fat particles and the plastic fragments is possible. For the separation between these two classes linear combinations of all image planes have been used. To demonstrate the possibility of automated identification of (plastic) impurities in meat processing, a classification model is calculated. First, training pixels are selected from the stack of images as described in the previous paragraph. The selected pixels are labeled in 4 classes: plastic, fat, dark colored meat and light colored meat. These pixels are used to calculate the classification model. Linear Discriminant Analysis (LDA) is selected as supervised classification technique. The result of the LDA classification is shown in figure 8. The plastic fragments are correctly identified and discriminated from the fat particles. This example clearly shows that automated identification of impurities, which are transparent or difficult to see in the visible wavelength range, can be detected with multivariate imaging systems in the visi ble and near infrared region.
4. CONCLUSION The availability of exploration and classification tools for multivariate imaging allows maximum extraction of physicochemical and spatial information. The resulting classification models can be applied in agrotechnological process inspection and control. Experiments indicate that multivariate imaging systems have a surplus value above traditional vision systems.
5. ACKNOWLEDGEMENT The authors acknowledge the contribution of G. Otten for the implementation of the neural networks, R. van Soest and F. Golbach for the implementation of the magic wand and G. Polder from 307