Image dense matching based on region growth with adaptive window

Abstract

A new and efficient dense matching algorithm is presented based on region growth, which can be applied to a wide range of image pairs including those with large disparity or without rectification. Firstly, some points in the image pair are matched using a new two-level matching method. These points are taken as the seeds from which corresponding relations propagate towards other regions of the images under two strategies. By introducing the statistic model of disparity distribution within the window, modified SSD is produced and adopted as the cost function. The size of the template window is adaptive with textures within it and the size of the search window is changed in inverse proportion to the confidence coefficient. The algorithm has been tested with real stereo images and the results demonstrate its accuracy and efficiency.