场景无关约束下的特征匹配算法

鉴于对极约束是立体图像中完全不依赖于场景的重要几何约束，因此在特征匹配中起着很重要的作用，而且由于同形映射描述了平面场景的立体图像之间的对应关系，故大量文献中利用它对平面场景的立体图像对进行特征匹配。为了提高立体图像匹配精度和速度，提出了一种改进的场景无关约束下的特征匹配算法，该算法针对用对极约束和同形映射来进行曲面场景匹配的过程中同形估计容易出现降阶的情况，通过引入区域面积检测法来避免降阶情况的发生，以改善匹配结果；同时，由于在同形矩阵估计中，通过加入基础矩阵和同形矩阵本质上的约束关系，可使得原本独立的同形约束和对极约柬关系很好地融入到匹配的整个过程中，从而快速有效地抑制了错误匹配的发生。对真实图像的实验分析证明，该改进算法具有迭代次数少、速度更快和匹配精度高的良好性能。

Feature Correspondence Algorithm Based on Scene-independent Constraint

The epipolar geometry is the fundamental and important constraint between stereo image pair. It is independent of scene structure and plays a very important role in feature correspondence, Homography characterizes the correspondences between the two views due to the same plane scene which is extensively applied in plane scenes. To avoid the degenerate configuration when directly computing the homography given the epipolar geometry in curve scene, the area-detection approach is presented in this paper. In addition ,the constraint relation between homography matrix and fundamental matrix is involved in the homography estimation. The two schemes can greatly improve the matching precision and efficiency of the algorithm, Experimental results with real image data have illustrate the performance of the schemes.