Stereo matching algorithm based on modified wavelet decomposition process

Multiresolutional representation such as pyramidal structures is useful for stereo matching as coarse-to-fine strategy. However, conventional pyramidal structures using Gaussian or Laplacian filters lose much information due to their low-pass filtering characteristics and also cannot obtain any spatial orientation selectivity. The adoption of wavelet transform can remedy these problems, but at the time of image translation, it changes wavelet coefficients. In this paper, a pyramid using modified wavelet decomposition process is proposed to have translation invariance. The image transformed by the proposed method is converted into appropriate multiple features without loss of information. Since the importance of each feature is determined heuristically in the multiple feature-based stereo matching method, it is very difficult to fuse them adequately. In the proposed algorithm, the weight of each feature, that is, the relative importance of each feature, is decided from the similarity between the intensities in the local region of each left and right wavelet channels. Since the window size used for the decision of weight and disparity values greatly influences the processed result, the window is adaptively determined from the disparities estimated in the coarse resolution and low-varying channel of fine resolution. The window size must be large enough to obtain signal-to-noise ratio, but not too large as to induce the effects of projective distortion. Also, a new relaxation algorithm which can reduce false matches without blurring the disparity edge is proposed. By integrating adaptive weight variable window selection method, and relaxation process, an accurate and stable disparity map is obtained. Experimental results for various images show that the proposed algorithm has good performance even if the image has the unfavorable conditions. (C) 1997 Pattern Recognition Society.