基于兴趣点伪泽尼克矩的图像拼接

针对基于特征匹配的传统图像拼接方法对旋转和噪声敏感的问题,提出了一种基于兴趣点伪泽尼克(Zernike)矩的图像自动拼接技术.利用哈里斯(Harris)角检测器获取图像中的兴趣点,计算以兴趣点为中心邻域窗口的伪泽尼克矩,通过比较各个兴趣点邻域伪泽尼克矩的欧氏距离提取出初始特征点对,根据几何变换模型剔除伪特征点对,最后利用得到的几何变换模型,对输入图像进行几何变换后将两幅图像间的重叠区域进行图像融合,完成图像的拼接.实验表明,该方法对平移、任意角度的旋转以及噪声均具有鲁棒性,对于具有小尺度变换(小于1.5)的图像仍然具有很好的拼接效果.

Image Mosaic Technique Based on Pseudo-Zernike Moments of Interest Points

The traditional feature-based algorithm was found to be sensitive to rotations and noise,and an automatic image mosaic technique based on pseudo-Zernike moments of interest points was proposed.Firstly the Harris corner detector was used to gain the interest points,and the pseudo-Zernike moments defined on the interest point neighborhood were computed.Through comparing the Euclidean distance of these pseudo-Zernike moments to extract the initial feature points pair,the spurious feature points pair were rejected by geometric transform model.After the geometric transform of input images,the overlapping region of two images was fused and the image stitching was finished.Experimental results demonstrate that the proposed algorithm is robust to translation,rotation,noise and slight scaling(under 1.5).