基于特征点概率模型与匹配的图像拼接算法

Image mosaic algorithm based on feature points probability model and matching

为了解决图像内容单一、特征点不明显且数量少而导致其难以拼接准确的问题,提出了基于特征点概率与匹配的图像拼接算法。首先,利用图像重叠区域特征的单应性,开发出对旋转、尺度及光照不变的可靠特征几何结构,计算焦距矩阵与旋转矩阵,实现特征点检测;利用随机抽样一致性算法,完成特征点匹配。然后,利用伯努利分布特性和贝叶斯计算,建立内点和离群点的模型概率,剔除错误匹配点,从而提高图像匹配精度,准确完成图像拼接。最后以条码对接是否准确为图像拼接质量判断基准,实验测试结果显示:与当前图像拼接技术相比,该算法拥有更高的拼接准确率与鲁棒性。

In order to solve the image content and single feature points is not obvious and the number of little and lead to its difficult to splice the exact problem, put forward the image stitching algorithm based on probability and matching feature points. Should first, using the image characteristics of the overlapping area, developed for rotation, scale and illumination invariant feature geometry structure, reliable calculating distance matrix and the rotation matrix, realize the feature point detection; Using random sampling consistency algorithm, feature point matching. Then, using the Bernoulli distribution characteristics, and calculation of the bayesian model points and outliers in probability, eliminate error matching points, so as to improve the image matching accuracy, accurate finish image stitching. Last barcode docking is accurate as benchmark image splicing quality judgment, experimental test results show that compared with the current image splicing technology, the joining together of this algorithm has higher accuracy and robustness.