摘要
图像配准是图像分析的关键技术,针对经典Lucas-Kanade算法计算量大的缺点,提出了一种基于梯度下降的图像配准算法,该方法通过互换目标图像与模板图像的功能,重新定义了目标函数,采用Gauss-Newton梯度下降法求解参数增量,得到在整个迭代过程中保持恒定且可预先求得的海森矩阵,利用迭代法求解变换系数,直至满足收敛条件。实验结果表明,该方法在保证精确度的条件下,提高了计算效率。
Image registration is a key technique in image analysis. This paper begins with an analysis of the shortcomings of the Lucas-Kanade algorithm and the existing improved algorithms. Aiming at the shortcomings of these algorithms, that is, huge computational cost, an image registration algorithm based on gradient descent is presented. Firstly, the algorithm redefines the objective function by switching the role of the image and the template. Then, the Gauss-Newton gradient descent algorithm is used to get the increments of the parameter. Since there is nothing in the Hessian matrix that depends on the parameter, it is constant in every iteration and can be pre-computed. Finally, the parameter is iteratively solved until it satisfied the test for convergence. Experiments with several standard sequences and using the set of affine warp which is adapted to any combinations of the template＇s rotation, zooming and translation indicate our new algorithm＇s capability. With the same description of the template it achieves the same precision as the Lucas-Kanade algorithm. The comparison of computational cost between our algorithm and the Lucas-Kanade algorithm demonstrates the improvement of our algorithm.
出处
《西北工业大学学报》
EI
CAS
CSCD
北大核心
2007年第5期642-645,共4页
Journal of Northwestern Polytechnical University
基金
航空科学基金(04I53069)
西北工业大学"英才培养计划"资助
关键词
图像配准
海森矩阵
仿射变换
image registration, Hessian matrix, affine warp
