| 1. | Algorithms of eliminating the mismatching points in scene matching guidance system
景像匹配误匹配点的剔除算法 |
| 2. | Camera self - calibration algorithm from matched - point sets on two orthogonal planes
基于双正交平面上匹配点对的摄像机自标定算法 |
| 3. | For the sake of sparseness of edge pixels , the matching area may big enough to find an accurate corresponding edge pixel
由于边缘点的稀疏，匹配时选用的区域可以大些，可以得到准确的匹配点。 |
| 4. | Experiment state clearly the effects of outliers can be removed by the algorithm which is robust and better results have been obtained
实验表明该算法能消除图像匹配点错误对结果带来的影响，鲁棒性较好。 |
| 5. | Using the pwfm , it is easy to locate matching points accurately , and it yields fewer spurious responses than correlation matching . it is robust for almost all the images
该算法的优点是匹配点定位简易、准确，虚警少，稳健性强，适用于各类图象的匹配识别。 |
| 6. | So it is a key problem to find the joint points in successive photos . in the paper , a new matching algorithm based on feature pixel - lined segment is first put forward
因此，找到相邻照片的匹配点，实现图象的自动拼接是基于图象的图形绘制技术中的关键问题。 |
| 7. | This method search rough matching position by fewer points , and then search more precise matching position by more points in the neighbor of rough matching position
该算法利用较少的点来搜索较为粗略的匹配位置，然后在这不很精确的匹配点附近利用较多的点搜索较为精确的匹配位置。 |
| 8. | After analyzing the real match position and false match position from the correlation surface , the new method can search the real match position based on the feature of the peak on the correlation surface
由灰度相关计算得到的相关面，通过分析真实匹配位置和虚假匹配位置局部极大值峰的特性，找到了一种基于峰值特征检测寻找真实匹配点的方法。 |
| 9. | In the algorithm , we look the image - matching problem as a kind of optimization problem that finds a most suitable matching point in matched image . then we use the strong global optimization capability of evolutionary computation to solve the complicated matching problem
该算法将图像匹配问题看作为寻求最优匹配点的寻优问题，然后利用进化算法的强大的全局寻优性能来对图像进行匹配。 |
| 10. | Finally , the only available geometric constraint , namely , the epipolar constraint , is exploited robustly using the above initial set of matches . more accurate matches are eventually found , as in stereo matching , by using the recovered epipolar geometry
最后，利用初始匹配点对快速、稳健地估计出了立体图像对之间的唯一几何约束- - -对极几何约束，然后利用对极几何约束改进初始匹配。 |
