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A Hybrid Stereo Matching Algorithm Using Wavelet Modulus Maxima Based on Feature and Area Process

In this paper, a hybrid stereo matching algorithm which is based on feature and area process (HAFA) is presented. At first edge features are extracted and matched using wavelet transform modulus maxima representation to get a sparse disparity map. In this step, edge points are detected by getting the maxima modulus of the wavelet transform of the stereo image. At coarse scales, the local maxima of modules have different positions and only detected sharp edges because of the smoothing of the images. At fine scale, there are many maxima created by the image noise. We get rid of the influence of the noise by adding a threshold to the process of finding the maxima. And we introduce the normalized cross correlation criteria into the discrete wavelet transform domain to match the features. Then pixels outside the edge are matched using area-based algorithm under the constraint of the acquired disparity map. The HAFA can inherit the accuracy of feature-based approaches and can simplify the searching procedure of area-based method. The dense disparity map produced by the HAFA indicate that this algorithm is effective and of great value