高斯平滑下引线框架的多尺度噪声图像快速配准算法

Multi-scale Fast Registration Algorithm for Noise Image of Lead Frame Under Gauss Smoothing

为满足机器视觉系统的实时性要求,提出一种新的快速图像配准算法,即高斯多尺度快速注册算法(GMFR)。GMFR综合图像空间域互相关算法(CC)与频域互功率谱算法(CPS)在配准精度和效率上的优势,并在图像多尺度表达的基础上,通过引入高斯平滑来避免图像亚采样过程中的细节丢失问题。为了对配准算法进行定量分析,进一步定义性能函数,随图像规模的增大算法的性能优势得到成倍提高。在真实引线框架图像上的实验结果表明,即使针对复杂噪声图像,GMFR仍具备较高的配准准确率和较强的鲁棒性,与传统的CC以及CPS图像配准方法相比,该算法效率明显提高。

In order to fulfill the real-time requirement for machine vision system,this paper proposes a new fast image registration method,i. e. Gauss Multi-scale Fast Registration（ GMFR）. The GMFR algorithm integrates the advantages of registration accuracy and efficiency from Cross Correlation（ CC） algorithm in image spatial domain,as well as Cross Power Spectrum（ CPS） algorithm in image frequency domain. Based on image multi-scale representation,the image details can be protected via Gauss smoothing during image down sampling. A performance function is further defined in order to perform quantitative analysis of the registration algorithm, and as the image size increases,the efficiency of this new algorithm is improved exponentially. Experimental results on real lead frame images demonstrate that,the proposed GMFR method still has high registration accuracy and strong robustness, even for the complex noise images. Moreover,compared with the traditional CC and CPS image registration methods,the efficiency of the algorithm is significantly improved.