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Amplitude elimination for stereo image matching based on the wavelet approach

Jaworska T.

Machine Graphics and Vision

2005

Vol. 14, No. 1

103-120

Point-to-point correspondence is one of the most challenging problems in stereo image matching. Correspondence or disparity established between points of two images is the result of stereo matching. The paper presents new point-to-point correspondence algorithm based on wavelet analysis. Each image in the pair is decomposed into an approximation, and details go through the coarse to fine level. For the above decomposition, multi resolution analysis is used. In the proposed approach, a disparity is found in the wavelet transform space. An extension and generalization of phase-based method is presented. The classical Gabor's approach is extended to real wavelets. Differences of amplitudes (grey level) in images which frequently appear in stereo pair are eliminated. Invariance of the disparity determination with respect to amplitude changes may be achieved by choosing an appropriate pair of wavelet systems. The achieved result is broader than the classical one, based Gabor wavelet and the phase method. Numerical experiments with images have confirmed this approach . Finally, three concepts (see Section 5) are presented to analyse the problem of disparity determination globally.

A new approach to stereo image matching based on multiresolution wavelet analysis

Jaworska T.

Machine Graphics and Vision

2000

Vol. 9, No. 1/2

439-446

Automatic stereo matching in an importamt problem and many algorithms have been developed already. The paper presents a new stereo matching algorithm. Correspondence and disparity esablished between points. of two images is the result of stereo matching. The proposed solution is based on wavelet transform. A two-dimensional digital image is a signal which is analyzed in wanelet space. Each image of stereopair is decomposed using multiresolution analysis for many levels from fine-to-coarse. Each image is also decomposed into an approximation and details (vertical, horizontal and diagonal) which are computed using a recursive algorithm of Mallat [8]. In the proposed approach a disparuty is found in the wavelet transform space. The minimization problem which appears in disparity determination is solved using a Hopfield neural network. The neural network is used for finding the disparity separately for approximation and details in the horizontal direction and separately for aproximation and details in the vertical direction. The approach is based on constructing vectors containing coefficients from each level. The number of vectors is equal to the numbers of coefficients at the finest level. Vectors are constructed for coefficients of approximation and for sum of coefficients of approximation and details respectively. A norm of the difference of vectors is set as neurons bias.