Abstract: Template matching based on Normalized Cross-Correlation（NCC） measure has found application in a broad range of image processing. The ridge of the NCC results can be sharpened for the template matching with image after edge detection through taking full advantage of the image space correlation. To improve the localization accuracy, and to meet the requirements for real-time, space constraints and flexible adaption to different mission phases with different image matching strategies in missile automatic target recognition, combined with the Sobel edge detection, an improved high-speed parallel architecture for real-time implementation of template matching with NCC, using Field Programmable Gate Array（FPGA） is proposed. In this architecture, several novel efficient approaches are proposed to reduce logic resource usage and computation time. A comparative study of the template matching before and after image edge detection has shown that image edge detection can sharpen the ridge of the NCC results. Function and timing simulation and practical experiment for automatic target recognition applied in infrared missile guidance system have shown that this architecture can effectively improve the speed performance of the practical system with little extra computation time. The hardware implementation based on FPGA can reduce the system size.

摘要： 基于归一化互相关测度（NCC）的模板匹配已经在图像处理领域得到了广泛的应用。对图像进行边缘检测然后进行模板匹配，可充分利用图像的空间相关性，锐化模板匹配结果的相关峰，提高匹配的准确度，可以获得更高的定位精度。为了有效提高定位精度，考虑到导弹制导系统的算法实时性、体积以及为适应战场不同任务阶段采用不同匹配策略的灵活性要求，基于FPGA，通过结合Sobel边缘检测，进一步改进了提出的图像归一化互相关模板匹配高速并行实现架构。对边缘检测前后图像模板匹配的仿真比较结果表明，边缘检测处理可有效锐化相关峰；基于Altera的FPGA芯片EP2S90和开发软件Quartus II 8.0的并行实现架构功能与时序仿真及在实际目标识别系统中的应用表明，这种方案可有效地提高系统的运算速度和定位精度，FPGA实现本身也进一步缩小了系统的体积。