| 1. |
- Verikas, Antanas, 1951-, et al.
(author)
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A New method for colour measurements in graphic arts
- 1999
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In: Color Research and Application. - New York : Wiley-Blackwell. - 0361-2317 .- 1520-6378. ; 24:3, s. 185-196
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Journal article (peer-reviewed)abstract
- 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.
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| 2. |
- Gelzinis, Adas, et al.
(author)
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Quality function for unsupervised classification and its use in graphic arts
- 1999
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In: Journal of Advanced Computational Intelligence. - Japan. ; 3:6, s. 532-540
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Journal article (peer-reviewed)abstract
- In this paper, we propose quality function for an unsupervised neural classification. The function is based on the third order polynomials. The objective of the quality function is to find a place of the input space sparse in data points. By maximising the quality function, we find decision boundary between data clusters instead of centres of the clusters. The shape and place of the decision boundary are rather insensitive to the magnitude of the weight vector established during the maximisation process. A superiority of the proposed quality function over other similar functions as well as conventional clustering algorithms tested has been observed in the experiments. The proposed quality function has been successfully used for colour image segmentation.
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| 3. |
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| 4. |
- Stasiunas, Antanas, et al.
(author)
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A multi-channel adaptive nonlinear filtering structure realizingsome properties of the hearing system
- 2005
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In: Computers in Biology and Medicine. - Amsterdam : Elsevier. - 0010-4825 .- 1879-0534. ; 35:6, s. 495-510
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Journal article (peer-reviewed)abstract
- An adaptive nonlinear signal-filtering model of the cochlea is proposed based on the functional properties of the inner ear. The model consists of the cochlear filtering segments taking into account the longitudinal, transverse and radial pressure wave propagation. On the basis of an analytical description of different parts of the model and the results of computer modeling, the biological significance of the nonlinearity of signal transduction processes in the outer hair cells, their role in signal compression and adaptation, the efferent control over the characteristics of the filtering structures (frequency selectivity and sensitivity) are explained. © 2004 Elsevier Ltd. All rights reserved.
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| 5. |
- Stasiunas, Antanas, et al.
(author)
-
A non-linear circuit for simulating OHC of the cochlea
- 2003
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In: Medical Engineering and Physics. - London : Elsevier. - 1350-4533 .- 1873-4030. ; 25:7, s. 591-601
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Journal article (peer-reviewed)abstract
- In the present paper, referring to known characteristics of the outer hair cells functioning in the cochlea of the inner ear, a functional model of the outer hair cells is constructed. It consists of a linear feed-forward circuit and a non-linear positive feedback circuit. The feed-forward circuit reflects the contribution of local basilar and tectorial membrane areas and passive outer hair cells’ physical parameters to the forming of low-selectivity resonance characteristics. The non-linear positive feedback circuit reflects the non-linear outer hair cell signal transduction processes and the active role of efferents from the medial superior olive in altering circuit sensitivity and selectivity.Referring to an analytical description of the circuit model and computer simulation results, an explanation is given over the biological meaning of the outer hair cells’ non-linearities in signal transduction processes and the role of the non-linearities in achieving the following: signal compression, the dependency of circuit sensitivity and frequency selectivity upon the input signal amplitude, the compatibility of high-frequency selectivity and short transient response of the biological filtering circuits.
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| 6. |
- Stasiunas, Antanas, et al.
(author)
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Compression, adaptation and efferent control in a revised outer hair cell functional model
- 2005
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In: Medical Engineering and Physics. - Amsterdam : Elsevier. - 1350-4533 .- 1873-4030. ; 27:9, s. 780-789
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Journal article (peer-reviewed)abstract
- In the cochlea of the inner ear, outer hair cells (OHC) together with the local passive structures of the tectorial and basilar membranes comprise non-linear resonance circuits with the local and central (afferent–efferent) feedback. The characteristics of these circuits and their control possibilities depend on the mechanomotility of the OHC. The main element of our functional model of the OHC is the mechanomotility circuit with the general transfer characteristic y = k tanh(x − a). The parameter k of this characteristic reflects the axial stiffness of the OHC, and the parameter a working position of the hair bundle. The efferent synaptic signals act on the parameter k directly and on the parameter a indirectly through changes in the membrane potential. The dependences of the sensitivity and selectivity on changes in the parameters a and k are obtained by the computer simulation. Functioning of the model at low-level input signals is linear. Due to the non-linearity of the transfer characteristic of the mechanomotility circuit the high-level signals are compressed. For the adaptation and efferent control, however, the transfer characteristic with respect to the initial operating point should be asymmetrical (a > 0). The asymmetry relies on the deflection of the hair bundle from the axis of the OHC.
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| 7. |
- Verikas, Antanas, 1951-, et al.
(author)
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Colour classification by neural networks in graphic arts
- 1998
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In: Neural Computing & Applications. - London : Springer London. - 0941-0643 .- 1433-3058. ; 7:1, s. 52-64
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Journal article (peer-reviewed)abstract
- 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.
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| 8. |
- Verikas, Antanas, 1951-, et al.
(author)
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Colour image segmentation by modular neural network
- 1997
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In: Pattern Recognition Letters. - Amsterdam : Elsevier. - 0167-8655 .- 1872-7344. ; 18:2, s. 173-185
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Journal article (peer-reviewed)abstract
- 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.
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| 9. |
- Verikas, Antanas, 1951-, et al.
(author)
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Colour speck counter for assessing the dirt level in secondary fibre pulps
- 2003
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In: Journal of Pulp and Paper Science (JPPS). - Montreal : Pulp and Paper Technical Association of Canada. - 0826-6220. ; 29:7, s. 220-224
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Journal article (peer-reviewed)abstract
- Speck count is increasingly used as a parameter to assess the quality of secondary fibre pulps. The resolution of most of the commercial image analysis systems is too low for detecting small specks. Therefore, small specks are not taken into consideration when using conventional image analysis systems to assess pulp quality. We have recently developed a colour speck counter which can detect specks ranging in size from ∼5 to 300 μm. In this paper, we present the results of experimental investigations related to the use of the speck counter to assess the dirt level in secondary fibre pulps. We assume an exponential speck size distribution and advocate the idea of using the scale parameter λ of the distribution to characterize the size content of a set of specks detected. Experimental investigations performed have shown that the scale parameter, together with the expected speck area and the speck number, can be used to characterize and rank secondary fibre pulps according to dirt level and the dirt-size distribution.
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| 10. |
- Verikas, Antanas, 1951-, et al.
(author)
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Combining neural networks, fuzzy sets, and evidence theory based approaches for analysing colour images
- 2000
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In: IJCNN 2000. - Los Alamitos : IEEE Computer Society. - 9780769506197 - 0769506194 - 0780365410 - 9780780365414 - 0769506216 - 9780769506210 ; , s. 297-302
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Conference paper (peer-reviewed)abstract
- This paper presents an approach to determining colours of specks in an image taken from a pulp sample. The task is solved through colour classification by an artificial neural network. The network is trained using possibilistic target values. The problem of post-processing of a pixelwise-classified image is addressed from the point of view of the Dempster-Shafer theory of evidence. Each neighbour of a pixel being analysed is considered as an item of evidence supporting particular hypotheses regarding the class label of that pixel. The experiments performed have shown that the colour classification results correspond well with the human perception of colours of the specks.
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