Amplitude elimination for stereo image matching based on the wavelet approach

• Autorzy: Jaworska T.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

stereo matching; point-to-point correspondence; wavelet transform; amplitude elimination; disparity determination

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2005
• Tom: Vol. 14, No. 1
• Strony: 103--120
• Opis fizyczny: Bibliogr. 13 poz., rys., wykr.

Twórcy

autor

Jaworska T.

• Systems Research Institute, Polish Academy of Sciences, Newelska 6 Str, 01-447 Warsaw, Poland,

Bibliografia

• [1] Marr D., Poggio T.: Cooperative Computation of stereo disparity. Science, 194, 283-287, 1976.
• [2] Crowley J. L., Parker A. C.: A representation for shape based on peaks and trough in the difference of low-pass transform. IEEE Trans. PAMI, 6, 156-169, 1984.
• [3] Waxman A. M., Ullman S.: Contour evolution, neighbourhood deformation and global image flow: Planar surfaces in motion. Institute Journal Robotics Researches, 4, 95-108, 1985.
• [4] Zhou Y. T., Chappella R.: Neural network approach to stereo matching. SPIE Applications of Digital Image Processing, October, 243-250, 1988.
• [5] Mallat S.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE PAMI, Vol. 11, No. 7, July, 674-693, 1989.
• [6] Langley K. et al.: Vertical and horizontal disparities from phase. 1st ECC, Springer-Verlag, Antibes, 315-325, 1990.
• [7] Fleet D. J., Jepson A. D.: Stability of Phase Information. IEEE Trans. PAMI 15 (12), 1253-1268, 1993.
• [8] Fleet D. J.: Disparity from Local Weighted Phase-Correlation. IEEE Int. Conf. on Systems, Man and Cybernetics, San Antonio, USA, October, 48-56, 1994.
• [9] Hellwich O., Faig W.: Graph-Based Feature Matching Using Descriptive and Relational Parameters. PE&RS, 60(4), April, 443-450, 1994.
• [10] He-Ping P.: General Stereo Image Matching Using Symmetric Complex Wavelets. SPIE Wavelets Applications in Signal and Image Processing IV, Denver, USA, August, 1996.
• [11] Jaworska T.: Stereo image matching based on the wavelet transform. Proc. of the 13th Int. Conf. on System Science, Wroclaw, Poland, September, 279-284, 1998.
• [12] Mallat S.: A Wavelet Tour of Signal Processing. Academic Press, London, 1998.
• [13] Jaworska T.: New approach to stereo image matching based on multiresolution wavelet analysis. MG&V, 9 (1/2), Warsaw, Poland, May, 439-446, 2000.