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  • 101.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Bacauskiene, Marija
    Department of Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, Adas
    Department of Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
    Vaiciukynas, Evaldas
    Department of Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
    Uloza, Virgilijus
    Department of Otolaryngology, Kaunas University of Medicine, Kaunas, Lithuania.
    Questionnaire- versus voice-based screening for laryngeal disorders2012In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 39, no 6, p. 6254-6262Article in journal (Refereed)
    Abstract [en]

    The usefulness of questionnaire and voice data to screen for laryngeal disorders is explored. Answers to 14 questions form a questionnaire data vector. Twenty-three variables computed by the commercial "Dr.Speech" software from a digital voice recording of a sustained phonation of the vowel sound/a/constitute a voice data vector. Categorization of the data into a healthy class and two classes of disorders, namely diffuse and nodular mass lesions of vocal folds is the task pursued in this work. Visualization of data and automated decisions is also an important aspect of this work. To make the categorization, a support vector machine (SVM) is designed based on genetic search. Linear as well as nonlinear canonical correlation analysis (CCA) is employed, to study relations between the questionnaire and voice data sets. The curvilinear component analysis, performing nonlinear mapping into a two-dimensional space, is used for visualizing data and decisions. Data from 240 patients were used in the experimental studies. It was found that the questionnaire data provide more information for the categorization than the voice data. There are 3-4 common directions along which the statistically significant variations of the questionnaire and voice data occur. However, the linear relations between the variations occurring in the two data sets are not strong. On the other hand, very strong linear relations were observed between the nonlinear variates obtained from the questionnaire data and linear ones computed from the voice data. Questionnaire data carry great potential for preventive health care in laryngology. © 2011 Elsevier Ltd. All rights reserved.

  • 102.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Nilsson, Carl Magnus
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Soft Computing for Assessing the Quality of Colour Prints2006In: Advances in applied artificial intelligence: proceedings / [ed] Ali, M and Dapoigny, R., Berlin: Springer, 2006, p. 701-710Conference paper (Refereed)
    Abstract [en]

    We present a soft computing techniques based option for assessing the quality of colour prints. The values of several print distortion attributes are evaluated by employing data clustering, support vector regression, and image analysis procedures and then aggregated into an overall print quality measure using fuzzy integration. The experimental investigations performed have shown that the print quality evaluations provided by the measure correlate well with the print quality rankings obtained from the experts. The developed tools are successfully used in a printing shop for routine print quality control.

  • 103.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania.
    Nilsson, Carl-Magnus
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Estimating the amount of cyan, magenta, yellow, and black inks in arbitrary colour pictures2007In: Neural computing & applications (Print), ISSN 0941-0643, E-ISSN 1433-3058, Vol. 16, no 2, p. 187-195Article in journal (Refereed)
    Abstract [en]

    This paper is concerned with the offset lithographic colour printing. To obtain high quality colour prints, given proportions of cyan (C), magenta (M), yellow (Y), and black (K) inks (four primary inks used in the printing process) should be accurately maintained in any area of the printed picture. To accomplish the task, the press operator needs to measure the printed result for assessing the proportions and use the measurement results to reduce the colour deviations. Specially designed colour bars are usually printed to enable the measurements. This paper presents an approach to estimate the proportions directly in colour pictures without using any dedicated areas. The proportions—the average amount of C, M, Y, and K inks in the area of interest—are estimated from the CCD colour camera RGB (L*a*b*) values recorded from that area. The local kernel ridge regression and the support vector regression are combined for obtaining the desired mapping L*a*b* ⇒ CMYK, which can be multi-valued.

  • 104.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Gelzinis, Adas
    Department of Electrical & Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Department of Electrical & Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
    Mining data with random forests: A survey and results of new tests2011In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 44, no 2, p. 330-349Article in journal (Refereed)
    Abstract [en]

    Random forests (RF) has become a popular technique for classification, prediction, studying variable importance, variable selection, and outlier detection. There are numerous application examples of RF in a variety of fields. Several large scale comparisons including RF have been performed. There are numerous articles, where variable importance evaluations based on the variable importance measures available from RF are used for data exploration and understanding. Apart from the literature survey in RF area, this paper also presents results of new tests regarding variable rankings based on RF variable importance measures. We studied experimentally the consistency and generality of such rankings. Results of the studies indicate that there is no evidence supporting the belief in generality of such rankings. A high variance of variable importance evaluations was observed in the case of small number of trees and small data sets.

  • 105.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS). Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Hållander, Magnus
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Uloza, Virgilijus
    Kaunas University of Medicine, Kaunas, Lithuania.
    Kaseta, Marius
    Kaunas University of Medicine, Kaunas, Lithuania.
    Combining image, voice, and the patient's questionnaire data to categorize laryngeal disorders2010In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 49, no 1, p. 43-50Article in journal (Refereed)
    Abstract [en]

    Objective: This paper is concerned with soft computing techniques for categorizing laryngeal disorders based on information extracted from an image of patient's vocal folds, a voice signal, and questionnaire data.

    Methods: Multiple feature sets are exploited to characterize images and voice signals. To characterize colour, texture, and geometry of biological structures seen in colour images of vocal folds, eight feature sets are used. Twelve feature sets are used to obtain a comprehensive characterization of a voice signal (the sustained phonation of the vowel sound /a/). Answers to 14 questions constitute the questionnaire feature set. A committee of support vector machines is designed for categorizing the image, voice, and query data represented by the multiple feature sets into the healthy, nodular and diffuse classes. Five alternatives to aggregate separate SVMs into a committee are explored. Feature selection and classifier design are combined into the same learning process based on genetic search.

    Results: Data of all the three modalities were available from 240 patients. Among those, 151 patients belong to the nodular class, 64 to the diffuse class and 25 to the healthy class. When using a single feature set to characterize each modality, the test set data classification accuracy of 75.0%, 72.1%, and 85.0% was obtained for the image, voice and questionnaire data, respectively. The use of multiple feature sets allowed to increase the accuracy to 89.5% and 87.7% for the image and voice data, respectively. The test set data classification accuracy of over 98.0% was obtained from a committee exploiting multiple feature sets from all the three modalities. The highest classification accuracy was achieved when using the SVM-based aggregation with hyper parameters of the SVM determined by genetic search. Bearing in mind the difficulty of the task, the obtained classification accuracy is rather encouraging.

    Conclusions: Combination of both multiple feature sets characterizing a single modality and the three modalities allowed to substantially improve the classification accuracy if compared to the highest accuracy obtained from a single feature set and a single modality. In spite of the unbalanced data sets used, the error rates obtained for the three classes were rather similar.

  • 106.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania .
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania .
    Olenina, Irina
    Klaipeda University, Kaunas, Lithuania.
    Olenin, Sergej
    Klaipeda University, Klaipeda, Lithuania.
    Vaiciukynas, Evaldas
    Kaunas University of Technology, Kaunas, Lithuania .
    Automated image analysis- and soft computing-based detection of the invasive dinoflagellate Prorocentrum minimum (Pavillard) Schiller2012In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 39, no 5, p. 6069-6077Article in journal (Refereed)
    Abstract [en]

    A long term goal of this work is an automated system for image analysis- and soft computing-based detection, recognition, and derivation of quantitative concentration estimates of different phytoplankton species using a simple imaging system. This article is limited, however, to detection of objects in phytoplankton images, especially objects representing one invasive species-Prorocentrum minimum (P. minimum), which is known to cause harmful blooms in many estuarine and coastal environments. A new technique, combining phase congruency-based detection of circular objects, stochastic optimization, and image segmentation was developed for solving the task. The developed algorithms were tested using 114 images of 1280 × 960 pixels size recorded by a colour camera. There were 2088 objects representing P. minimum cells in the images in total. The algorithms were able to detect 93.25% of the objects. Bearing in mind simplicity of the imaging system used the result is rather encouraging and may be applied for future development of the algorithms aimed at automated classification of objects into classes representing different phytoplankton species. © 2011 Elsevier Ltd. All rights reserved.

  • 107.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab). Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania .
    Olenina, Irina
    Klaipeda University, Klaipeda, Lithuania.
    Olenin, Sergej
    Klaipeda University, Klaipeda, Lithuania .
    Vaiciukynas, Evaldas
    Kaunas University of Technology, Kaunas, Lithuania.
    Phase congruency-based detection of circular objects applied to analysis of phytoplankton images2012In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 45, no 4, p. 1659-1670Article in journal (Refereed)
    Abstract [en]

    Detection and recognition of objects representing the Prorocentrum minimum (P. minimum) species in phytoplankton images is the main objective of the article. The species is known to cause harmful blooms in many estuarine and coastal environments. A new technique, combining phase congruency-based detection of circular objects in images, stochastic optimization-based object contour determination, and SVM- as well as random forest (RF)-based classification of objects was developed to solve the task. A set of various features including a subset of new features computed from phase congruency preprocessed images was used to characterize extracted objects. The developed algorithms were tested using 114 images of 1280×960 pixels. There were 2088 P. minimum cells in the images in total. The algorithms were able to detect 93.25% of objects representing P. minimum cells and correctly classified 94.9% of all detected objects. The feature set used has shown considerable tolerance to out-of-focus distortions. The obtained results are rather encouraging and will be used to develop an automated system for obtaining abundance estimates of the species. © 2011 Elsevier Ltd All rights reserved.

  • 108.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Gelzinis, Adas
    Kaunas University of Technology, Department of Electrical and Control Equipment, Kaunas, Lithuania .
    Hållander, Magnus
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Bacauskiene, Marija
    Kaunas University of Technology, Department of Electrical and Control Equipment, Kaunas, Lithuania .
    Alzghoul, Ahmad
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Screening web breaks in a pressroom by soft computing2011In: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 11, no 3, p. 3114-3124Article in journal (Refereed)
    Abstract [en]

    The objective of this work is to identify the main parameters of the printing press, the printing process, and the paper affecting the occurrence of web breaks in a pressroom. Two approaches are explored. The first one treats the problem as a task of data classification into "break" and "non-break" classes. The procedures of classifier design and selection of relevant input variables are integrated into one process based on genetic search. The second approach, targeted for data visualization and also based on genetic search, combines procedures of input variable selection and data mapping into a two-dimensional space. The genetic search-based analysis has shown that the web tension parameters are amongst the most important ones. It was also found that the group of paper related parameters recorded online contain more information for predicting the occurrence of web breaks than the group of traditional parameters recorded off-line at a paper lab. Using the selected set of parameters, on average, 93.7% of the test set data were classified correctly. The average classification accuracy of web break cases was equal to 76.7%. (C) 2010 Elsevier B. V. All rights reserved.

  • 109.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Gelzinis, Adas
    Kaunas University of Technology, Lithuania.
    Malmqvist, Kerstin
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Using unlabelled data to train a multilayer perceptron2001In: Neural Processing Letters, ISSN 1370-4621, E-ISSN 1573-773X, Vol. 14, no 3, p. 179-201Article in journal (Refereed)
    Abstract [en]

    This Letter presents an approach to using both labelled and unlabelled data to train a multilayer perceptron. The unlabelled data are iteratively pre-processed by a perceptron being trained to obtain the soft class label estimates. It is demonstrated that substantial gains in classification performance may be achieved from the use of the approach when the labelled data do not adequately represent the entire class distributions. The experimental investigations performed have shown that the approach proposed may be successfully used to train neural networks for learning different classification problems.

  • 110.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab). Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania.
    Vaiciukynas, Evaldas
    Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Minelga, Jonas
    Kaunas University of Technology, Kaunas, Lithuania.
    Hållander, Magnus
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Uloza, Virgilijus
    Lithuanian University of Health Sciences, Kaunas, Lithuania.
    Padervinskis, Evaldas
    Lithuanian University of Health Sciences, Kaunas, Lithuania.
    Data dependent random forest applied to screening for laryngeal disorders through analysis of sustained phonation: Acoustic versus contact microphone2015In: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 37, no 2, p. 210-218Article in journal (Refereed)
    Abstract [en]

    Comprehensive evaluation of results obtained using acoustic and contact microphones in screening for laryngeal disorders through analysis of sustained phonation is the main objective of this study. Aiming to obtain a versatile characterization of voice samples recorded using microphones of both types, 14 different sets of features are extracted and used to build an accurate classifier to distinguish between normal and pathological cases. We propose a new, data dependent random forests-based, way to combine information available from the different feature sets. An approach to exploring data and decisions made by a random forest is also presented. Experimental investigations using a mixed gender database of 273 subjects have shown that the perceptual linear predictive cepstral coefficients (PLPCC) was the best feature set for both microphones. However, the linear predictive coefficients (LPC) and linear predictive cosine transform coefficients (LPCTC) exhibited good performance in the acoustic microphone case only. Models designed using the acoustic microphone data significantly outperformed the ones built using data recorded by the contact microphone. The contact microphone did not bring any additional information useful for the classification. The proposed data dependent random forest significantly outperformed the traditional random forest. (C) 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

  • 111.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Kalsyte, Zivile
    Department of Electrical and Control Instrumentation, Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Department of Electrical and Control Instrumentation, Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, Adas
    Department of Electrical and Control Instrumentation, Kaunas University of Technology, Kaunas, Lithuania.
    Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey2010In: Soft Computing - A Fusion of Foundations, Methodologies and Applications, ISSN 1432-7643, E-ISSN 1433-7479, Vol. 14, no 9, p. 995-1010Article in journal (Refereed)
    Abstract [en]

    This paper presents a comprehensive review of hybrid and ensemble-based soft computing techniques applied to bankruptcy prediction. A variety of soft computing techniques are being applied to bankruptcy prediction. Our focus is on techniques, namely how different techniques are combined, but not on obtained results. Almost all authors demonstrate that the technique they propose outperforms some other methods chosen for the comparison. However, due to different data sets used by different authors and bearing in mind the fact that confidence intervals for the prediction accuracies are seldom provided, fair comparison of results obtained by different authors is hardly possible. Simulations covering a large variety of techniques and data sets are needed for a fair comparison. We call a technique hybrid if several soft computing approaches are applied in the analysis and only one predictor is used to make the final prediction. In contrast, outputs of several predictors are combined, to obtain an ensemble-based prediction.

  • 112.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Lipnickas, Arunas
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Malmqvist, Kerstin
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Selecting neural networks for a committee decision2002In: International Journal of Neural Systems, ISSN 0129-0657, E-ISSN 1793-6462, Vol. 12, no 5, p. 351-361Article in journal (Refereed)
    Abstract [en]

    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.

  • 113.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Lipnickas, Arunas
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Malmqvist, Kerstin
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Gelzinis, Adas
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Soft combination of neural classifiers: a comparative study1999In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 20, no 4, p. 429-444Article in journal (Refereed)
    Abstract [en]

    This paper presents four schemes for soft fusion of the outputs of multiple classifiers. In the first three approaches, the weights assigned to the classifiers or groups of them are data dependent. The first approach involves the calculation of fuzzy integrals. The second scheme performs weighted averaging with data-dependent weights. The third approach performs linear combination of the outputs of classifiers via the BADD defuzzification strategy. In the last scheme, the outputs of multiple classifiers are combined using Zimmermann's compensatory operator. An empirical evaluation using widely accessible data sets substantiates the validity of the approaches with data-dependent weights, compared to various existing combination schemes of multiple classifiers.

  • 114.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Lundström, Jens
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Bacauskiene, Marija
    Department of Electrical and Control Equipment, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania.
    Gelzinis, Adas
    Department of Electrical and Control Equipment, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania.
    Advances in computational intelligence-based print quality assessment and control in offset colour printing2011In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 38, no 10, p. 13441-13447Article in journal (Refereed)
    Abstract [en]

    Nowadays most of information processing steps in printing industry are highly automated, except the last one – print quality assessment and control. Usually quality assessment is a manual, tedious, and subjective procedure. This article presents a survey of non numerous developments in the field of computational intelligence-based print quality assessment and control in offset colour printing. Recent achievements in this area and advances in applied computational intelligence, expert and decision support systems lay good foundations for creating practical tools to automate the last step of the printing process.

  • 115.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS). Department of Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
    Malmqvist, Kerstin
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Bacauskiene, Marija
    Department of Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
    Combining neural networks, fuzzy sets, and evidence theory based approaches for analysing colour images2000In: IJCNN 2000: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, Como, Italy, 24-27 July 2000, Vol. 2 / [ed] Shun Ichi-Amari, C. Lee Giles, Marco Gori & Vincenzo Piuri, Los Alamitos: IEEE Computer Society, 2000, p. 297-302Conference paper (Refereed)
    Abstract [en]

    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.

  • 116.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Malmqvist, Kerstin
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Combining neural networks, fuzzy sets, and the evidence theory based techniques for detecting colour specks2001In: Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, E-ISSN 1875-8967, Vol. 10, no 2, p. 117-130Article in journal (Refereed)
    Abstract [en]

    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.

  • 117.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Malmqvist, Kerstin
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Bergman, Lars
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Nilsson, Kenneth
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Hierarchical neural network for color classification1994In: The 1994 IEEE International Conference on Neural Networks: IEEE World Congress on Computational Intelligence, June 27-June 29, 1994, Walt Disney World Dolphin Hotel, Orlando, Florida, Vol. 5 / [ed] IEEE, Piscataway, NJ: IEEE Press, 1994, p. 2938-2941Conference paper (Refereed)
    Abstract [en]

    To make the hierarchical architecture, the neural networks of different type and different unsupervised learning techniques were combined. The classification accuracy obtained from such architecture is high enough to use it in the print quality control.

  • 118.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Malmqvist, Kerstin
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Bergman, L.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Neural networks based colour measuring for process monitoring and control in multicoloured newspaper printing2000In: Neural computing & applications (Print), ISSN 0941-0643, E-ISSN 1433-3058, Vol. 9, no 3, p. 227-242Article in journal (Refereed)
    Abstract [en]

    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.

  • 119.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Malmqvist, Kerstin
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Bergman, Lars
    Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-3031 Kaunas, Lithuania.
    Detecting and measuring rings in banknote images2005In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 18, no 3, p. 363-371Article in journal (Refereed)
    Abstract [en]

    Various intelligent systems show a rapidly growing potential use of visual information processing technologies. This paper presents an example of employing visual information processing technologies for detecting and measuring rings in banknote images. The size of the rings is one of parameters used to inspect the banknote printing quality. The approach developed consists of two phases. In the first phase, based on histogram processing and data clustering, image areas containing rings are localized and edges of the rings are detected. Then, in the second phase, applying the hard and possibilistic spherical shell clustering to the extracted edge pixels the ring centre and radii are estimated. The experimental investigations performed have shown that even highly occluded rings are robustly detected. Several prototypes of the system developed have been installed in two banknote printing shops in Europe.

  • 120.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Malmqvist, Kerstin
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Bergman, Lars
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Engstrand, P.
    Center of Excellence, Holmen Paper AB, Norrkoping, Sweden.
    Colour speck counter for assessing the dirt level in secondary fibre pulps2003In: Journal of Pulp and Paper Science (JPPS), ISSN 0826-6220, Vol. 29, no 7, p. 220-224Article in journal (Refereed)
    Abstract [en]

    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.

  • 121.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Malmqvist, Kerstin
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Malmqvist, L.
    Department of Atomic Physics, University of Lund.
    Bergman, L.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    A New method for colour measurements in graphic arts1999In: Color Research and Application, ISSN 0361-2317, E-ISSN 1520-6378, Vol. 24, no 3, p. 185-196Article in journal (Refereed)
    Abstract [en]

    This article presents a method for colour measurements directly on printed half-tone multicoloured pictures. The article introduces the concept of colour impression. By this concept we mean the CMY or CMYK vector (colour 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. The colour vector expresses integrated information about the tonal values and ink densities. Values of the colour vector components increase if tonal values or ink densities rise and vice versa. If, for some primary colour, the ink density and tonal value do not change, the corresponding component of the colour vector remains constant. If some reference values of the colour vector components are set from a preprint, then, after an appropriate calibration, the colour vector directly shows how much the operator needs to raise or lower the cyan, magenta, yellow, and black ink densities in order to correct colours of the picture being measured. The values of the components are obtained by registering the RGB image from the measuring area and then transforming the set of registered RGB values to the triplet or quadruple of CMY or CMYK values, respectively. Algorithms based on artificial neural networks are used for performing the transformation. During the experimental investigations, we have found a good correlation between components of the colour vector and ink densities.

  • 122.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Uloza, Virgilijus
    Department of Otolaryngology, Kaunas University of Medicine, Kaunas 50009, Lithuania.
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, Studentu 50, Kaunas 51368, Lithuania.
    Gelzinis, Adas
    Department of Applied Electronics, Kaunas University of Technology, Studentu 50, Kaunas 51368, Lithuania.
    Kelertas, Edgaras
    Department of Applied Electronics, Kaunas University of Technology, Studentu 50, Kaunas 51368, Lithuania.
    Advances in laryngeal imaging2009In: European Archives of Oto-Rhino-Laryngology, ISSN 0937-4477, E-ISSN 1434-4726, Vol. 266, no 10, p. 1509-1520Article in journal (Refereed)
    Abstract [en]

    Imaging and image analysis became an important issue in laryngeal diagnostics. Various techniques, such as videostroboscopy, videokymography, digital kymograpgy, or ultrasonography are available and are used in research and clinical practice. This paper reviews recent advances in imaging for laryngeal diagnostics.

  • 123.
    Xiang, Gao
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Nan, Jiang
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Supervised Methods for Fault Detection in Vehicle2010Independent thesis Advanced level (degree of Master (One Year)), 15 credits / 22,5 HE creditsStudent thesis
    Abstract [en]

    Uptime and maintenance planning are important issues for vehicle operators (e.g.operators of bus fleets). Unplanned downtime can cause a bus operator to be fined if the vehicle is not on time. Supervised classification methods for detecting faults in vehicles are compared in this thesis. Data has been collected by a vehicle manufacturer including three kinds of faulty states in vehicles (i.e. charge air cooler leakage, radiator and air filter clogging). The problem consists of differentiating between the normal data and the three different categories of faulty data. Evaluated methods include linear model, neural networks model, 1-nearest neighbor and random forest model. For every kind of model, a variable selection method should be used. In our thesis we try to find the best model for this problem, and also select the most important input signals. After we compare these four models, we found that the best accuracy (96.9% correct classifications) was achieved with the random forest model.

  • 124.
    You, Liwen
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Almost Linear Biobasis Function Neural Networks2007In: The 2007 International Joint Conference on Neural Networks: IJCNN 2007 conference proceedings : August 12-17, 2007, Resaissance Orlando Resort, Orlando, Florida, USA, Piscataway, N.J.: IEEE Press, 2007, p. 1774-1778Conference paper (Other academic)
    Abstract [en]

    An analysis of biobasis function neural networks is presented, which shows that the similarity metric used is a linear function and that bio-basis function neural networks therefore often end up being just linear classifiers in high dimensional spaces. This is a consequence of four things: the linearity of the distance measure, the normalization of the distance measure, the recommended default values of the parameters, and that biological data sets are sparse.

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