| 1. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Colour speck counter for assessing the dirt level in secondary fibre pulps
- 2003
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Ingår i: Journal of Pulp and Paper Science (JPPS). - Montreal : Pulp and Paper Technical Association of Canada. - 0826-6220. ; 29:7, s. 220-224
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Tidskriftsartikel (refereegranskat)abstract
- Speck count is increasingly used as a parameter to assess the quality of secondary fibre pulps. The resolution of most of the commercial image analysis systems is too low for detecting small specks. Therefore, small specks are not taken into consideration when using conventional image analysis systems to assess pulp quality. We have recently developed a colour speck counter which can detect specks ranging in size from ∼5 to 300 μm. In this paper, we present the results of experimental investigations related to the use of the speck counter to assess the dirt level in secondary fibre pulps. We assume an exponential speck size distribution and advocate the idea of using the scale parameter λ of the distribution to characterize the size content of a set of specks detected. Experimental investigations performed have shown that the scale parameter, together with the expected speck area and the speck number, can be used to characterize and rank secondary fibre pulps according to dirt level and the dirt-size distribution.
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| 2. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Combining neural networks, fuzzy sets, and evidence theory based approaches for analysing colour images
- 2000
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Ingår i: IJCNN 2000. - Los Alamitos : IEEE Computer Society. - 9780769506197 - 0769506194 - 0780365410 - 9780780365414 - 0769506216 - 9780769506210 ; , s. 297-302
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Konferensbidrag (refereegranskat)abstract
- This paper presents an approach to determining colours of specks in an image taken from a pulp sample. The task is solved through colour classification by an artificial neural network. The network is trained using possibilistic target values. The problem of post-processing of a pixelwise-classified image is addressed from the point of view of the Dempster-Shafer theory of evidence. Each neighbour of a pixel being analysed is considered as an item of evidence supporting particular hypotheses regarding the class label of that pixel. The experiments performed have shown that the colour classification results correspond well with the human perception of colours of the specks.
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| 3. |
- Verikas, Antanas, 1951-, et al.
(författare)
-
Combining neural networks, fuzzy sets, and the evidence theory based techniques for detecting colour specks
- 2001
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Ingår i: Journal of Intelligent & Fuzzy Systems. - Amsterdam : IOS Press. - 1064-1246 .- 1875-8967. ; 10:2, s. 117-130
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Tidskriftsartikel (refereegranskat)abstract
- An approach to detecting colour specks in an image taken from a pulp sample of recycled paper is presented. The task is solved through pixel-wise colour classification by an artificial neural network and post-processing based on the evidence theory. The network is trained using possibilistic target values, which are determined through a self-organising process in a 2D and 1D map of chromaticity and lightness, respectively. The problem of post-processing of a pixelwise-classified image is addressed from the point of view of the Dempster-Shafer theory of evidence. Each neighbour of a pixel being analysed is considered as an item of evidence supporting particular hypotheses regarding the class label of that pixel. The strength of support is defined as a function of the degree of uncertainty in class label of the neighbour, and the distance between the neighbour and the pixel being considered. The experiments performed have shown that the colour classification results correspond well with the human perception of colours of the specks.
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| 4. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Learning an Adaptive Dissimilarity Measure for Nearest Neighbour Classification
- 2003
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Ingår i: Neural Computing & Applications. - London : Springer. - 0941-0643 .- 1433-3058. ; 11:3-4, s. 203-209
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, an approach to weighting features for classification based on the nearest-neighbour rules is proposed. The weights are adaptive in the sense that the weight values are different in various regions of the feature space. The values of the weights are found by performing a random search in the weight space. A correct classification rate is the criterion maximised during the search. Experimentally, we have shown that the proposed approach is useful for classification. The weight values obtained during the experiments show that the importance of features may be different in different regions of the feature space
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| 5. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Monitoring the de-inking process through neural network-based colour image analysis
- 2000
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Ingår i: Neural Computing & Applications. - New York, USA : Springer-Verlag New York. - 0941-0643 .- 1433-3058. ; 9:2, s. 142-151
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents an approach to determining the colours of specks in an image of a pulp being recycled. The task is solved through colour classification by an artificial neural network. The network is trained using fuzzy possibilistic target values. The number of colour classes found in the images is determined through the self-organising process in the two-dimensional self-organising map. The experiments performed have shown that the colour classification results correspond well with human perception of the colours of the specks.
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| 6. |
- Verikas, Antanas, 1951-, et al.
(författare)
-
Neural modelling and control of the offset printing process
- 2003
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Ingår i: Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence, NCI 2003, May 19-21, 2003, Cancun, Mexico. - Calgary : ACTA Press. - 0889863474 ; , s. 130-135
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Konferensbidrag (refereegranskat)abstract
- In this paper, we present an approach to neural modelling and control of the offset lithographic printing process. A committee of neural networks is trained to measure the printing process output - the observable variables. From only one measurement the trained committee is capable of estimating the actual relative amount of each cyan, magenta, yellow, and black inks dispersed on paper in the measuring area. The obtained measurements are then further used by a neural model predictive control unit for generating control signals to compensate for colour deviation in offset newspaper printing. The experimental investigations performed have shown that the system developed achieves a higher printing process control accuracy than that usually obtained by the press operator.
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| 7. |
- Verikas, Antanas, 1951-, et al.
(författare)
-
Neural networks based colour measuring for process monitoring and control in multicoloured newspaper printing
- 2000
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Ingår i: Neural Computing & Applications. - London : Springer. - 0941-0643 .- 1433-3058. ; 9:3, s. 227-242
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a neural networks based method and a system for colour measurements on printed halftone multicoloured pictures and halftone multicoloured bars in newspapers. The measured values, called a colour vector, are used by the operator controlling the printing process to make appropriate ink feed adjustments to compensate for colour deviations of the picture being measured from the desired print. By the colour vector concept, we mean the CMY or CMYK (cyan, magenta, yellow and black) vector, which lives in the three- or four-dimensional space of printing inks. Two factors contribute to values of the vector components, namely the percentage of the area covered by cyan, magenta, yellow and black inks (tonal values) and ink densities. Values of the colour vector components increase if tonal values or ink densities rise, and vice versa. If some reference values of the colour vector components are set from a desired print, then after an appropriate calibration, the colour vector measured on an actual halftone multicoloured area directly shows how much the operator needs to raise or lower the cyan, magenta, yellow and black ink densities to compensate for colour deviation from the desired print. The 18 months experience of the use of the system in the printing shop witnesses its usefulness through the improved quality of multicoloured pictures, the reduced consumption of inks and, therefore, less severe problems of smearing and printing through.
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| 8. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Selecting features for neural network committees
- 2002
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Ingår i: Proceedings of the International Joint Conference on Neural Networks. - Piscataway : IEEE. - 0780372786 ; , s. 215-220
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Konferensbidrag (refereegranskat)abstract
- We present a neural network based approach for identifying salient features for classification in neural network committees. Our approach involves neural network training with an augmented cross-entropy error function. The augmented error function forces the neural network to keep low derivatives of the transfer functions of neurons of the network when learning a classification task. Such an approach reduces output sensitivity to the input changes. Feature selection is based on the reaction of the cross-validation data set classification error due to the removal of the individual features. We compared the approach with two other neural network based feature selection methods. The algorithm developed outperformed the methods by achieving a higher classification accuracy on three real world problems tested. ©2002 IEEE
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| 9. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Selecting features with neural networks
- 2001
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Ingår i: Neural Information Precessing. - Shanghai : Fudan University Press. - 7309030125 ; , s. 63-68
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Konferensbidrag (refereegranskat)abstract
- We present a neural network based approach for identifying salient features for classification in feed-forward neural networks. Our approach involves neural network training with an augmented cross-entropy error function. The augmented error function forces the neural network to keep low derivatives of the transfer functions of neurons when learning a classification task. Such an approach reduces output sensitivity to the input changes. Feature selection is based on the reaction of the cross-validation data set classification error due to the removal of the individual features. We compared the approach with five other feature selection methods, each of which banks on different concept. The algorithm developed outperformed the other methods by achieving a higher classification accuracy on all the problems tested.
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| 10. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Selecting neural networks for a committee decision
- 2002
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Ingår i: International Journal of Neural Systems. - Singapore : World Scientific. - 0129-0657 .- 1793-6462. ; 12:5, s. 351-361
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Tidskriftsartikel (refereegranskat)abstract
- To improve recognition results, decisions of multiple neural networks can be aggregated into a committee decision. In contrast to the ordinary approach of utilizing all neural networks available to make a committee decision, we propose creating adaptive committees, which are specific for each input data point. A prediction network is used to identify classification neural networks to be fused for making a committee decision about a given input data point. The jth output value of the prediction network expresses the expectation level that the jth classification neural network will make a correct decision about the class label of a given input data point. The proposed technique is tested in three aggregation schemes, namely majority vote, averaging, and aggregation by the median rule and compared with the ordinary neural networks fusion approach. The effectiveness of the approach is demonstrated on two artificial and three real data sets.
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