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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 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.

  • 102.
    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.

  • 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, LT-51368 Kaunas, Lithuania.
    Valincius, Donatas
    Department of Applied Electronics, Kaunas University of Technology, LT-51368 Kaunas, Lithuania.
    Gelzinis, Adas
    Department of Applied Electronics, Kaunas University of Technology, LT-51368 Kaunas, Lithuania.
    Predictor output sensitivity and feature similarity-based feature selection2008In: Fuzzy sets and systems (Print), ISSN 0165-0114, E-ISSN 1872-6801, Vol. 159, no 4, p. 422-434Article in journal (Refereed)
    Abstract [en]

    This paper is concerned with a feature selection technique capable of generating an efficient feature set in a few selection steps. The feature saliency measure proposed is based on two factors, namely, the fuzzy derivative of the predictor output with respect to the feature and the similarity between the feature being considered and the feature set. The use of the fuzzy derivative enables modelling the vagueness that occurs in estimating the predictor output sensitivity. The feature similarity measure employed allows avoiding utilization of very redundant features. The experimental investigations performed on five real world problems have shown the effectiveness of the feature selection technique proposed. The technique developed removed a large number of features from the original data sets without reducing the classification accuracy of a classifier. In contrast, the accuracy of the classifiers utilizing the reduced feature sets was higher than those exploiting all the original features.

  • 104.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Gelzinis, Adas
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Training neural networks by stochastic optimisation2000In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 30, no 1-4, p. 153-172Article in journal (Refereed)
    Abstract [en]

    We present a stochastic learning algorithm for neural networks. The algorithm does not make any assumptions about transfer functions of individual neurons and does not depend on a functional form of a performance measure. The algorithm uses a random step of varying size to adapt weights. The average size of the step decreases during learning. The large steps enable the algorithm to jump over local maxima/minima, while the small ones ensure convergence in a local area. We investigate convergence properties of the proposed algorithm as well as test the algorithm on four supervised and unsupervised learning problems. We have found a superiority of this algorithm compared to several known algorithms when testing them on generated as well as real data.

  • 105.
    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.

  • 106.
    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.

  • 107.
    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.

  • 108.
    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.

  • 109.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. 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.
    Bacauskiene, Marija
    Department of Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
    Olenina, Irina
    Department of Marine Research of the Environmental Protection Agency, Klaipeda, Lithuania & Marine Science and Technology Center, Klaipeda University, Klaipeda, Lithuania.
    Vaiciukynas, Evaldas
    Department of Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
    An Integrated Approach to Analysis of Phytoplankton Images2015In: IEEE Journal of Oceanic Engineering, ISSN 0364-9059, E-ISSN 1558-1691, Vol. 40, no 2, p. 315-326Article in journal (Refereed)
    Abstract [en]

    The main objective of this paper is detection, recognition, and abundance estimation of objects representing the Prorocentrum minimum (Pavillard) Schiller (P. minimum) species in phytoplankton images. The species is known to cause harmful blooms in many estuarine and coastal environments. The proposed technique for solving the task exploits images of two types, namely, obtained using light and fluorescence microscopy. Various image preprocessing techniques are applied to extract a variety of features characterizing P. minimum cells and cell contours. Relevant feature subsets are then selected and used in support vector machine (SVM) as well as random forest (RF) classifiers to distinguish between P. minimum cells and other objects. To improve the cell abundance estimation accuracy, classification results are corrected based on probabilities of interclass misclassification. The developed algorithms were tested using 158 phytoplankton images. There were 920 P. minimum cells in the images in total. The algorithms detected 98.1% of P. minimum cells present in the images and correctly classified 98.09% of all detected objects. The classification accuracy of detected P. minimum cells was equal to 98.9%, yielding a 97.0% overall recognition rate of P. minimum cells. The feature set used in this work has shown considerable tolerance to out-of-focus distortions. Tests of the system by phytoplankton experts in the cell abundance estimation task of P. minimum species have shown that its performance is comparable or even better than performance of phytoplankton experts exhibited in manual counting of artificial microparticles, similar to P. minimum cells. The automated system detected and correctly recognized 308 (91.1%) of 338 P. minimum cells found by experts in 65 phytoplankton images taken from new phytoplankton samples and erroneously assigned to the P. minimum class 3% of other objects. Note that, due to large variations of texture and size of P. minimum cells as well as- background, the task performed by the system was more complex than that performed by the experts when counting artificial microparticles similar to P. minimum cells.

  • 110.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Gelzinis, Adas
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Uloza, Virgilijus
    Department of Otolaryngology, Kaunas University of Medicine, Lithuania.
    Integrating global and local analysis of color, texture and geometrical information for categorizing laryngeal images2006In: International journal of pattern recognition and artificial intelligence, ISSN 0218-0014, Vol. 20, no 8, p. 1187-1205Article in journal (Refereed)
    Abstract [en]

    An approach to integrating the global and local kernel-based automated analysis of vocal fold images aiming to categorize laryngeal diseases is presented in this paper. The problem is treated as an image analysis and recognition task. A committee of support vector machines is employed for performing the categorization of vocal fold images into healthy, diffuse and nodular classes. Analysis of image color distribution, Gabor filtering, cooccurrence matrices, analysis of color edges, image segmentation into homogeneous regions from the image color, texture and geometry view point, analysis of the soft membership of the regions in the decision classes, the kernel principal components based feature extraction are the techniques employed for the global and local analysis of laryngeal images. Bearing in mind the high similarity of the decision classes, the correct classification rate of over 94% obtained when testing the system on 785 vocal fold images is rather encouraging.

  • 111.
    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.
    Bacauskiene, Marija
    Kaunas University of Technology, Lithuania.
    Uloza, Virgilijus
    Kaunas University of Medicine, Kaunas, Lithuania.
    Intelligent vocal cord image analysis for categorizing laryngeal diseases2005In: Innovations in applied artificial intelligence / [ed] Moonis Ali, Floriana Esposito, Springer, 2005, p. 69-78Conference paper (Refereed)
    Abstract [en]

    Colour, shape, geometry, contrast, irregularity and roughness of the visual appearance of vocal cords are the main visual features used by a physician to diagnose laryngeal diseases. This type of examination is rather subjective and to a great extent depends on physician’s experience. A decision support system for automated analysis of vocal cord images, created exploiting numerous vocal cord images can be a valuable tool enabling increased reliability of the analysis, and decreased intra- and inter-observer variability. This paper is concerned with such a system for analysis of vocal cord images. Colour, texture, and geometrical features are used to extract relevant information. A committee of artificial neural networks is then employed for performing the categorization of vocal cord images into healthy, diffuse, and nodular classes. A correct classification rate of over 93% was obtained when testing the system on 785 vocal cord images.

  • 112.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Gelzinis, Adas
    Department of Applied Electronics, Kaunas University of Technology, LT-3031, Kaunas, Lithuania.
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, LT-3031, Kaunas, Lithuania.
    Uloza, Virgilijus
    Kaunas University of Medicine, Kaunas, Lithuania.
    Towards a computer-aided diagnosis system for vocal cord diseases2006In: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 36, no 1, p. 71-84Article in journal (Refereed)
    Abstract [en]

    OBJECTIVE: The objective of this work is to investigate a possibility of creating a computer-aided decision support system for an automated analysis of vocal cord images aiming to categorize diseases of vocal cords. METHODOLOGY: The problem is treated as a pattern recognition task. To obtain a concise and informative representation of a vocal cord image, colour, texture, and geometrical features are used. The representation is further analyzed by a pattern classifier categorizing the image into healthy, diffuse, and nodular classes. RESULTS: The approach developed was tested on 785 vocal cord images collected at the Department of Otolaryngology, Kaunas University of Medicine, Lithuania. A correct classification rate of over 87% was obtained when categorizing a set of unseen images into the aforementioned three classes. CONCLUSION: Bearing in mind the high similarity of the decision classes, the results obtained are rather encouraging and the developed tools could be very helpful for assuring objective analysis of the images of laryngeal diseases.

  • 113.
    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.
    Bacauskiene, Marija
    Kaunas University of Technology.
    Uloza, Virgilijus
    Kaunas University of Medicine.
    Towards noninvasive screening for malignant tumours in human larynx2010In: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 32, no 1, p. 83-89Article in journal (Refereed)
    Abstract [en]

    This article is concerned with soft computing-based noninvasive screening for malignant disorders in human larynx. The suitability of two types of data for the analysis is explored. The questionnaire data and the digital voice recordings of the sustained phonation of the vowel sound /a/ are the data types considered in this study. The screening is considered as a task of data classification into the healthy, cancerous, and noncancerous classes. To explore data and decisions a nonlinear mapping technique exhibiting the property of local data ordering is applied. The classification accuracy of over 92% was obtained for unseen questionnaire data collected from 240 subjects. The experimental investigations have shown that, concerning the three classes, the questionnaire data carry much more discriminative information than the voice signal. Two-dimensional plots created using the mapping technique provide further insights into the data and decisions obtained from the classifiers.

  • 114.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Gelzinis, Adas
    Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania.
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania.
    Uloza, Virgilijus
    Department of Otolaryngology, Kaunas University of Medicine, LT-50009 Kaunas, Lithuania.
    Kaseta, Marius
    Department of Otolaryngology, Kaunas University of Medicine, LT-50009 Kaunas, Lithuania.
    Using the patient's questionnaire data to screen laryngeal disorders2009In: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 39, no 2, p. 148-155Article in journal (Refereed)
    Abstract [en]

    This paper is concerned with soft computing techniques for screening laryngeal disorders based on patient's questionnaire data. By applying the genetic search, the most important questionnaire statements are determined and a support vector machine (SVM) classifier is designed for categorizing the questionnaire data into the healthy, nodular and diffuse classes. To explore the obtained automated decisions, the curvilinear component analysis (CCA) in the space of decisions as well as questionnaire statements is applied. When testing the developed tools on the set of data collected from 180 patients, the classification accuracy of 85.0% was obtained. Bearing in mind the subjective nature of the data, the obtained classification accuracy is rather encouraging. The CCA allows obtaining ordered two-dimensional maps of the data in various spaces and facilitates the exploration of automated decisions provided by the system and determination of relevant groups of patients for various comparisons.

  • 115.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Gelzinis, Adas
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Valincius, Donata
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Uloza, Virgilijus
    Kaunas University of Medicine, Lithuania.
    A kernel-based approach to categorizing laryngeal images2007In: Computerized Medical Imaging and Graphics, ISSN 0895-6111, E-ISSN 1879-0771, Vol. 31, no 8, p. 587-594Article in journal (Refereed)
    Abstract [en]

    This paper is concerned with an approach to automated analysis of vocal fold images aiming to categorize laryngeal diseases. Colour, texture, and geometrical features are used to extract relevant information. A committee of support vector machines is then employed for performing the categorization of vocal fold images into healthy, diffuse, and nodular classes. The discrimination power of both, the original and the space obtained based on the kernel principal component analysis is investigated. A correct classification rate of over 92% was obtained when testing the system on 785 vocal fold images. Bearing in mind the high similarity of the decision classes, the correct classification rate obtained is rather encouraging.

  • 116.
    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.

  • 117.
    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.
    Kovalenko, Marina
    Kaunas University of Technology.
    Bacauskiene, Marija
    Kaunas University of Technology.
    Selecting features from multiple feature sets for SVM committee-based screening of human larynx2010In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 37, no 10, p. 6957-6962Article in journal (Refereed)
    Abstract [en]

    This paper is concerned with a two stage procedure for designing a sequential SVM committee and selecting features for the committee from multiple feature sets. It is assumed that features of one type comprise one feature set. Selection of both features and hyper-parameters of SVM classifiers comprising the committee is integrated into one learning process based on genetic search. The designing process focuses on feature selection for pair-wise classification implemented by the SVM. In the first stage, a series of pair-wise SVM are designed starting from the original feature sets as well as from sets created by simple random selection from the original ones. Outputs of the SVM are then converted into probabilities and used as inputs to the second stage SVM. When testing the technique in a three-class classification problem of voice data, a statistically significant improvement in classification accuracy was obtained if compared to parallel committees. The number of feature types and features selected for the pair-wise classification are class specific.

  • 118.
    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, Kaunas, Lithuania.
    Malmqvist, Kerstin
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Using Labelled and Unlabelled Data to Train a Multilayer Perceptron for Colour Classification in Graphic Arts1999In: Multiple approaches to intelligent systems: 12th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems IEA/AIE-99, Cairo, Egypt, May 31 - June 3, 1999. Proceedings / [ed] Ibrahim Imam, Yves Kodratoff, Ayman El-Dessouki and Moonis Ali, Berlin: Springer Berlin/Heidelberg, 1999, p. 550-559Conference paper (Refereed)
    Abstract [en]

    This paper presents an approach to using both labelled and unlabelled data to train a multi-layer 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 networks for colour classification in graphic arts.

  • 119.
    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.

  • 120.
    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.

  • 121.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Gelzinis, Adas
    Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania.
    Valincius, Donatas
    Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania.
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-51368 Kaunas, Lithuania.
    Uloza, Virgilijus
    Department of Otolaryngology, Kaunas University of Medicine, LT-50009 Kaunas, Lithuania.
    Multiple feature sets based categorization of laryngeal images2007In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 85, no 3, p. 257-266Article in journal (Refereed)
    Abstract [en]

    This paper is concerned with an automated analysis of laryngeal images aiming to categorize the images into three decision classes, namely healthy, nodular, and diffuse. The problem is treated as an image analysis and classification task. Aiming to obtain a comprehensive description of laryngeal images, multiple feature sets exploiting information on image colour, texture, geometry, image intensity gradient direction, and frequency content are extracted. A separate support vector machine (SVM) is used to categorize features of each type into the decision classes. The final image categorization is then obtained based on the decisions provided by a committee of support vector machines. Bearing in mind a high similarity of the decision classes, the correct classification rate of over 94% obtained when testing the system on 785 laryngeal images recorded at the Department of Otolaryngology, Kaunas University of Medicine is rather promising.

  • 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´ laboratory.
    Guzaitis, Jonas
    Kaunas University of Technology.
    Gelzinis, Adas
    Kaunas University of Technology.
    Bacauskiene, Marija
    Kaunas University of Technology.
    A general framework for designing a fuzzy rule-based classifier2011In: Knowledge and Information Systems, ISSN 0219-1377, E-ISSN 0219-3116, Vol. 29, no 1, p. 203-221Article in journal (Refereed)
    Abstract [en]

    This paper presents a general framework for designing a fuzzyrule-based classifier. Structure and parameters of the classifierare evolved through a two-stage genetic search. To reduce the searchspace, the classifier structure is constrained by a tree createdusing the evolving SOM tree algorithm. Salient input variables arespecific for each fuzzy rule and are found during the genetic searchprocess. It is shown through computer simulations of four real worldproblems that a large number of rules and input variables can beeliminated from the model without deteriorating the classificationaccuracy. By contrast, the classification accuracy of unseen data isincreased due to the elimination.This paper presents a general framework for designing a fuzzyrule-based classifier. Structure and parameters of the classifierare evolved through a two-stage genetic search. To reduce the searchspace, the classifier structure is constrained by a tree createdusing the evolving SOM tree algorithm. Salient input variables arespecific for each fuzzy rule and are found during the genetic searchprocess. It is shown through computer simulations of four real worldproblems that a large number of rules and input variables can beeliminated from the model without deteriorating the classificationaccuracy. By contrast, the classification accuracy of unseen data isincreased due to the elimination.

  • 123.
    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.

  • 124.
    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.
    Fusing neural networks through space partitioning and fuzzy integration2002In: Neural Processing Letters, ISSN 1370-4621, E-ISSN 1573-773X, Vol. 16, no 1, p. 53-65Article in journal (Refereed)
    Abstract [en]

    To improve recognition results, decisions of multiple neural networks can be aggregated into a committee decision. Aggregation weights assigned to neural networks or groups of networks can be the same in the entire data space or can be different (data dependent) in various regions of the space. In this paper, we propose a method for obtaining data dependent aggregation weights. The proposed approach is tested in two aggregation schemes, namely aggregation through neural network selection, and aggregation by the Choquet integral with respect to the lambda-fuzzy measure. The effectiveness of the approach is demonstrated on two artificial and three real data sets.

  • 125.
    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.

  • 126.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Lipnickas, Arunas
    Kaunas University of Technology, Department of Applied Electronics, Studentu 50, 3031, Kaunas, Lithuania.
    Malmqvist, Kerstin
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Selecting neural networks for making a committee decision2002In: ARTIFICIAL NEURAL NETWORKS - ICANN 2002 / [ed] Dorronsoro, J R, Berlin: Springer Berlin/Heidelberg, 2002, Vol. 2415, p. 420-425Conference paper (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 effectiveness of the approach is demonstrated on two artificial and three real data sets.

  • 127.
    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.

  • 128.
    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.

  • 129.
    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).
    Increasing colour image segmentation accuracy by means of fuzzy post-processing1995In: 1995 IEEE International Conference on Neural Networks: Proceedings, the University of Western Australia, Perth, Western Australia, 27 November-1 December 1995 (Vol. 4), Piscataway, NJ: IEEE Press, 1995, p. 1713-1718Conference paper (Refereed)
    Abstract [en]

    This paper presents a colour image segmentation method which attains a high segmentation accuracy even when regions of the image that have to be separated are very similar in colour. The proposed method classifies pixels into colour classes. Competitive learning with `conscience' is used to learn reference patterns for the different colour classes. A nearest neighbour classification rule followed by a block of fuzzy post-processing attains a high classification accuracy even for very similar colour classes. A correct classification rate of 97.8% has been achieved when classifying two very similar black colours, namely, the black printed with a black ink and the black printed with a mixture of cyan, magenta and yellow inks.

  • 130.
    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).
    Bacauskiene, M.
    Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania .
    Bergman, Lars
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Monitoring the de-inking process through neural network-based colour image analysis2000In: Neural computing & applications (Print), ISSN 0941-0643, E-ISSN 1433-3058, Vol. 9, no 2, p. 142-151Article in journal (Refereed)
    Abstract [en]

    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.

  • 131.
    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.

  • 132.
    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.

  • 133.
    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.

  • 134.
    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.
    Lipnickas, Arunas
    Kaunas University of Technology, Kaunas, Lithuania.
    Soft fusion of neural classifiers1998In: ICONIP'98: The Fifth International Conference on Neural Information Processing, jointly with JNNS'98, the 1998 annual conference of the Japanese Neural Network Society : Kitakyushu, Japan, October 21-23, 1998 : proceedings, Volume 1 / [ed] Shiro Usui, Takashi Omori, Burke, VA: IOS Press, 1998, p. 195-198Conference paper (Refereed)
    Abstract [en]

    This paper presents three schemes for soft fusion of outputs of multiple neural classifiers. The weights assigned to classifiers or groups of them are data dependent. The first scheme performs linear combination of outputs of classifiers and, in fact, is the BADD defuzzification strategy. The second approach involves calculation of fuzzy integrals. The last scheme performs weighted averaging with data dependent weights. 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. The majority rule, combination by averaging, the weighted averaging, the Borda count, and the fuzzy integral have been used for the comparison.

  • 135.
    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.

  • 136.
    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).
    Bergman, L.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Signahl, M.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE).
    Colour classification by neural networks in graphic arts1998In: Neural computing & applications (Print), ISSN 0941-0643, E-ISSN 1433-3058, Vol. 7, no 1, p. 52-64Article in journal (Refereed)
    Abstract [en]

    This paper presents a hierarchical modular neural network for colour classification in graphic arts, capable of distinguishing among very Similar colour classes. The network performs analysis in a rough to fine fashion, and is able to achieve a high average classification speed and a low classification error. In the rough stage of the analysis, clusters of highly overlapping colour classes are detected Discrimination between such colour classes is performed in the next stage by using additional colour information from the surroundings of the pixel being classified. Committees of networks make decisions in the next stage. Outputs of members of the committees are adaptively fused through the BADD defuzzification strategy or the discrete Choquet fuzzy integral. The structure of the network is automatically established during the training process. Experimental investigations show the capability of the network to distinguish among very similar colour classes that can occur in multicoloured printed pictures. The classification accuracy obtained is sufficient for the network to be used for inspecting the quality of multicoloured prints.

  • 137.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Malmqvist, Kerstin
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE).
    Bergman, Lars
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE).
    Colour image segmentation by modular neural network1997In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 18, no 2, p. 173-185Article in journal (Refereed)
    Abstract [en]

    In this paper segmentation of colour images is treated as a problem of classification of colour pixels. A hierarchical modular neural network for classification of colour pixels is presented. The network combines different learning techniques, performs analysis in a rough to fine fashion and enables to obtain a high average classification speed and a low classification error. Experimentally, we have shown that the network is capable of distinguishing among the nine colour classes that occur in an image. A correct classification rate of about 98% has been obtained even for two very similar black colours.

  • 138.
    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.

  • 139.
    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.

  • 140.
    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).
    Busk, Hans
    Department of Physics, Lund University, Lund, Sweden .
    Image analysis and artificial neural network for colour classification in multicolour newspaper printing1994In: 1994 International Printing and Graphic Arts Conference: October 17-20, 1994 Chateau Halifax Hotel, Halifax, Canada, Atlanta: TAPPI Press , 1994, p. 89-92Conference paper (Refereed)
    Abstract [en]

    Automatic inspection of printed multicoloured screen pictures demands methods for colour classification of screen dots and pans of screen dots directly in an arbitrary picture. The paper describes a technique using colour image analysis and artificial neural network for inverse colour separation. For every arbitrary small part of a coloured picture it is determined which coloured inks that have been printed in that part. Special attention is paid to the problem of seperating between black colour produced by black ink and black colour produced by combining cyan, magenta and yellow ink. The technique is tested on multicoloured newsprint and a high correct colour classification rate has been demonstrated.

  • 141.
    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.

  • 142.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Kaunas University of Technology, Kaunas, Lithuania.
    Parker, James
    Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS).
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Olsson, M. Charlotte
    Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS).
    Exploring relations between EMG and biomechanical data recorded during a golf swing2017In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 88, p. 109-117Article in journal (Refereed)
    Abstract [en]

    Exploring relations between patterns of peak rotational speed of thorax, pelvis and arm, and patterns of EMG signals recorded from eight muscle regions of forearms and shoulders during the golf swing is the main objective of this article. The linear canonical correlation analysis, allowing studying relations between sets of variables, was the main technique applied. To get deeper insights, linear and nonlinear random forests-based prediction models relating a single output variable, e.g. a thorax peak rotational speed, with a set of input variables, e.g. an average intensity of EMG signals were used. The experimental investigations using data from 16 golfers revealed statistically significant relations between sets of input and output variables. A strong direct linear relation was observed between lin- ear combinations of EMG averages and peak rotational speeds. The coefficient of determination values R2 = 0 . 958 and R2 = 0 . 943 obtained on unseen data by the random forest models designed to predict peak rotational speed of thorax and pelvis , indicate high modelling accuracy. However, predictions of peak rotational speed of arm were less accurate. This was expected, since peak rotational speed of arm played a minor role in the linear combination of peak speeds. The most important muscles to predict peak rotational speed of the body parts were identified. The investigations have shown that the canon- ical correlation analysis is a promising tool for studying relations between sets of biomechanical and EMG data. Better understanding of these relations will lead to guidelines concerning muscle engagement and coordination of thorax, pelvis and arms during a golf swing and will help golf coaches in providing substantiated advices. ©2017 Elsevier Ltd. All rights reserved.

  • 143.
    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.

  • 144.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Uloza, Virgilijus
    Kaunas University of Medicine, Kaunas, Lithuania.
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania.
    Bacauskiene, Marija
    Kaunas University of Technology, Kaunas, Lithuania.
    Pribuisiene, Ruta
    Kaunas University of Medicine, Kaunas, Lithuania.
    Kaseta, Marius
    Kaunas University of Medicine, Kaunas, Lithuania.
    Exploiting image, voice, and patient's questionnaire for screening laryngeal disorders2009In: Proceedings of the 3rd Advanced Voice Function Assessement International Workshop (AVFA 2009), Madrid, 2009, p. 85-88Conference paper (Refereed)
    Abstract [en]

    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. Multiple feature sets are used to characterize images and voice signals. A committee of support vector machines (SVM) is designed for categorizing the data represented by the multiple feature sets into the healthy, nodular and diffuse classes. The feature selection and classifier design is combined into the same learning process based on genetic search. When testing the developed tools on the set of data collected from 240 patients, the classification accuracy of over 98.0% was obtained. Combination of the three modalities allowed to substantially improve the classification accuracy if compared to the highest accuracy obtained from a single modality.

  • 145.
    Verikas, Antanas
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Kaunas University of Technology, Kaunas, Lithuania.
    Vaiciukynas, Evaldas
    Kaunas University of Technology, Kaunas, Lithuania.
    Gelzinis, Adas
    Kaunas University of Technology, Kaunas, Lithuania.
    Parker, James
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS).
    Olsson, M. Charlotte
    Halmstad University, School of Business, Engineering and Science, Biological and Environmental Systems (BLESS).
    Electromyographic Patterns during Golf Swing: Activation Sequence Profiling and Prediction of Shot Effectiveness2016In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 16, no 4, article id 592Article in journal (Refereed)
    Abstract [en]

    This study analyzes muscle activity, recorded in an eight-channel electromyographic (EMG) signal stream, during the golf swing using a 7-iron club and exploits information extracted from EMG dynamics to predict the success of the resulting shot. Muscles of the arm and shoulder on both the left and right sides, namely flexor carpi radialis, extensor digitorum communis, rhomboideus and trapezius, are considered for 15 golf players (∼5 shots each). The method using Gaussian filtering is outlined for EMG onset time estimation in each channel and activation sequence profiling. Shots of each player revealed a persistent pattern of muscle activation. Profiles were plotted and insights with respect to player effectiveness were provided. Inspection of EMG dynamics revealed a pair of highest peaks in each channel as the hallmark of golf swing, and a custom application of peak detection for automatic extraction of swing segment was introduced. Various EMG features, encompassing 22 feature sets, were constructed. Feature sets were used individually and also in decision-level fusion for the prediction of shot effectiveness. The prediction of the target attribute, such as club head speed or ball carry distance, was investigated using random forest as the learner in detection and regression tasks. Detection evaluates the personal effectiveness of a shot with respect to the player-specific average, whereas regression estimates the value of target attribute, using EMG features as predictors. Fusion after decision optimization provided the best results: the equal error rate in detection was 24.3% for the speed and 31.7% for the distance; the mean absolute percentage error in regression was 3.2% for the speed and 6.4% for the distance. Proposed EMG feature sets were found to be useful, especially when used in combination. Rankings of feature sets indicated statistics for muscle activity in both the left and right body sides, correlation-based analysis of EMG dynamics and features derived from the properties of two highest peaks as important predictors of personal shot effectiveness. Activation sequence profiles helped in analyzing muscle orchestration during golf shot, exposing a specific avalanche pattern, but data from more players are needed for stronger conclusions. Results demonstrate that information arising from an EMG signal stream is useful for predicting golf shot success, in terms of club head speed and ball carry distance, with acceptable accuracy. Surface EMG data, collected with a goal to automatically evaluate golf player’s performance, enables wearable computing in the field of ambient intelligence and has potential to enhance exercising of a long carry distance drive.

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