Recognition of textured objects using optimal inverse resonant filtration
Kvyetnyy, R. N.
Sofina, O. Yu.
Bunyak, Yu. A.
Квєтний, Р. Н.
Софина, О. Ю.
Буняк, Ю. А.
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Recognition of textured objects is a typical problem in computer vision and pattern recognition. Usually are meeting two variations of this problem. The first is the recognition of a signed texture. The second is objects of interest recognition in image with textured background. We offer an approach for solving of the both problems with using of the inverse resonant filtration. A textured object or background dynamic space can be approximated by a set of principal eigenvectors in the form of resonantharmonic functions called as eigen harmonics (EH). The Inverse ResonantFilter (IRF) which is resonant in respect to EH series eliminates textured background and forms indicator signal of certain species. The IRF is founded on the approximation and extrapolation of the texture template signal by series of EH. Two methods of 2-dimensional (2D) EH parameter estimation are considered. The methods give estimates robust tobreaks and noise peakspresentedin textured image signal. The first method is based on the linear symmetry model and can be presented as a double linear prediction model. Additionally the condition of the unitary symmetry is used to provide stationarity and periodicity of the model. The second method is based on 2D correlation matrix splitting with projection into the subspace of principal harmonic components. Implementation of the IRF is considered in spatial and spectral domains. Discrete Fourier transform with eigen kernel (DFTEK) was used for design and realization of the high order IRF. The DFTEK has fractionally fast transform algorithm. Aligning image fragment phases improved inverse resonant filtration in the spectral domain. It was shown that the optimal approach to image filtration consists of an initial fast filtration in the spectral domain, followed by post-filtration of the image zones containing anomalous background variations using IRF in the spatial domain. It was shown that the IRF is invariant to shift transform. It can be invariant to affine and scale transform if instead initial texture and image are using their invariant pattern in the form of image surface geometry characteristics.
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