Pub. online:1 Jan 2011Type:Research ArticleOpen Access
Volume 22, Issue 2 (2011), pp. 259–278
We consider that the outer hair cells of the inner ear together with the local structures of the basilar membrane, reticular lamina and tectorial membrane form the primary filters (PF) of the second order. Taking into account a delay in transmission of the excitation signal in the cochlea and the influence of the Reissner membrane, we design a signal filtering system consisting of the PF with the common PF of the neighboring channels. We assess the distribution of the central frequencies 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 the PF. As an example, we present a filter bank comprising 20 Gaussian-type channels each consisting of five PF. The proposed filtering system can be useful for designing cochlear implants based on biological principles of signal processing in the cochlea.
Pub. online:1 Jan 2008Type:Research ArticleOpen Access
Volume 19, Issue 3 (2008), pp. 363–376
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.
Pub. online:1 Jan 2004Type:Research ArticleOpen Access
Volume 15, Issue 3 (2004), pp. 315–328
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.