针对目标在移动过程中实时视觉图像特征点提取与配准的不稳定性,提出一种多算法融合的改进配准方法.首先,采用双边滤波、Canny边缘检测及形态学处理方法得到具有较强顽健性特征的边缘周边检测区域并基于离散Gaussian-Hermite矩对SURF算法中的Hessian矩阵进行修正,重新定义特征描述向量,同时采用肯德尔系数对配准的特征点进行约束.其次,通过融合光谱辐射颜色不变量模型及I_SURF算法对实时视觉彩色图像进行配准.最后,将改进算法与目标自适应更新算法相结合,实现了移动目标在室内环境中的实时匹配.实验结果表明,在不同旋转尺度下,改进算法的静态图像配准较SURF算法具有较高配准精度,移动图像特征点提取及配准数量的稳定性达到97%以上.%For the unstability of the real-time visual image characteristic point extraction and matching for the mobile target, an improved registration method of the multiple algorithm-fusion was introduced. Firstly, the method of bilateral filtering, Canny edge detection and morphological processing, was adopted to get the more robust image's edge map. And the founded points were limited in this edge map, then the Hessian matrix of SURF based on the discrete Gaus-sian-Hermite moment was modified. The character description vector was redefined in the algorithm. Following the above analysis, the Kendall coefficient constraint was discussed in image matching characteristic points. Secondly, the spectral radiant color invariant model and the I_SURF algorithm were used to match the real-time color image. Finally, the improved algorithm was combined with the update algorithm of adaptive target to match the mobile target in indoor environment. The experimental results show that the static image registration accuracy of the improved algorithm is higher than that of the SURF algorithm, and also the stability of the mobile image feature points extraction and the regis-tration number have achieved over 97% under different rotating scales.
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