基于小波模极大值的混合立体匹配算法的仿真

Simulation of the Hybrid Stereo Matching Algorithm Based on Wavelet Modulus Maxima

该文提出一种新的,利用小波模极大值的基于特征和区域的混合立体匹配算法。首先详细地叙述了如何利用小波模极大值提取图像边缘,并用该点的小波模极大值和幅角作为这些边缘点的特征描述。并在图像边缘立体匹配的过程中,将以前用于图像灰度域的互相关函数应用于小波域,边缘点的视差仿真图显示,该边缘匹配算法取得了很好的效果。然后,在基于区域的匹配中,利用边缘匹配的结果,减少了匹配互相关的搜索的范围,大大减少了计算量,增加了正确率。最后,将两个视差图结合起来,就得到了最终的稠密的视差图。

In this paper, a hybrid stereo matching algorithm based on feature and area process （HAFA） is presented. At first ,the edge features are extracted and matched using wavelet transform modulus maxima representation to get a sparse disparity map. In this step, edge points are detect 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 detect 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 indicates that this algorithm is effective and of great value.