Seed point selection method for triangle constrained image matching propagation

In order to select proper seed points for triangle constrained image-matching propagation, this letter analyzes the affects of different numbers and different distributions of seed points on the image-matching results. The concept of distribution quality is introduced to quantify the distribution of seed points. An intensive experimental analysis is illustrated using two different stereo aerial images and, based on the experimental results, a seed point selection strategy for triangle constrained image-matching propagation is proposed. An automatic selection method is then introduced that gives good distribution quality for a defined number of seed points.

I. Introduction

Some stereo image-matching methods require a user-selected set of corresponding points (seed points) in the left and right images to initiate automated stereo matching routines [1]. For example, Tang et al. [6] presented a dense matching method by making use of a Voronoi diagram constructed by reliably matched seed points, with the whole image being divided into cells, each cell containing a seed point taken for propagation inside its region, and the eight corresponding neighboring points of each seed are found using the disparity of this centered point under the continuity constraint. Chen et al. [2] utilized pyramidal stereo matching for urban three-dimensional building modeling; they divided the original image into several areas, and selected the most reliably matched points in each area as the seed points of the up-layer image matching. Zhu et al. [8] presented a robust image-matching propagation method based on the self-adaptive triangle constraint, which relies on the initial corresponding triangulations formed by a few seed points in the stereo pairs.