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  • 1.
    Bacauskiene, Marija
    et al.
    Department of Applied Electronics, Kaunas University of Technology, LT-3031, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    The Evidence Theory Based Post-Processing of Colour Images2004In: Informatica (Vilnius), ISSN 0868-4952, E-ISSN 1822-8844, Vol. 15, no 3, p. 315-328Article in journal (Refereed)
    Abstract [en]

    The problem of post-processing of a classified image is addressed from the point of view of the Dempster-Shafer theory of evidence. Each neighbour of a pixel being analyzed 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. A post-processing window defines the neighbours. Basic belief masses are obtained for each of the neighbours and aggregated according to the rule of orthogonal sum. The final label of the pixel is chosen according to the maximum of the belief function.

  • 2.
    Guzaitis, Jonas
    et al.
    Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    An Efficient Technique to Detect Visual Defects in Particleboards2008In: Informatica (Vilnius), ISSN 0868-4952, E-ISSN 1822-8844, Vol. 19, no 3, p. 363-376Article in journal (Refereed)
    Abstract [en]

    This paper is concerned with the problem of image analysis based detection of local defects embedded in particleboard surfaces. Though simple, but efficient technique developed is based on the analysis of the discrete probability distribution of the image intensity values and the 2D discrete Walsh transform. Robust global features characterizing a surface texture are extracted and then analyzed by a pattern classifier. The classifier not only assigns the pattern into the quality or detective class, but also provides the certainty value attributed to the decision. A 100% correct classification accuracy was obtained when testing the technique proposed on a set of 200 images.

  • 3.
    Stasiunas, Antanas
    et al.
    Kaunas University of Technology, Lithuania.
    Verikas, Antanas
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Bacauskiene, Marija
    Kaunas University of Technology, Lithuania.
    Miliauskas, Rimvydas
    Lithuanian University of Health Sciences, Lithuania.
    A Serial-Parallel Panoramic Filter Bank as a Model of Frequency Decomposition of Complex Sounds in the Human Inner Ear2011In: Informatica (Vilnius), ISSN 0868-4952, E-ISSN 1822-8844, Vol. 22, no 2, p. 259-278Article in journal (Refereed)
    Abstract [en]

    We consider that the outer hair cells of the inner ear together with the local structuresof the basilar membrane, reticular lamina and tectorial membrane form the primary filters (PF) ofthe second order. Taking into account a delay in transmission of the excitation signal in the cochleaand the influence of the Reissner membrane, we design a signal filtering system consisting of thePF with the common PF of the neighboring channels. We assess the distribution of the centralfrequencies of the channels along the cochlea, optimal number of the PF constituting a channel,natural frequencies of the channels, damping factors and summation weights of the outputs of thePF. As an example, we present a filter bank comprising 20 Gaussian-type channels each consistingof five PF. The proposed filtering system can be useful for designing cochlear implants based onbiological principles of signal processing in the cochlea.

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