摘要
本文提出一种改进的部分Hausdorff距离作为模板和图像中物体轮廓相似性的测度,较大地减少计算量。同时把遗传算法引入图像匹配识别,由于GA的高并行性和鲁棒性,可以较快地完成全局搜索,而不会陷入局部最优,因此GA和改进的Hausdorff距离相结合能有效地检测出具有平移、旋转和尺度变化的物体,以达到模板和图像间的有效匹配。
The paper provide that improved partial Hausdorff distance is to measure the degree of similarity between models and images outline,which can greatly reduce the computational complexity.GA is used to image march recognition, because GA has high parallel and robust performance,it will fast search global ,and it is not possible to get into partial optimum。The combination of GA and improved Hausdorff distance can be efficiently detected the objects that are changed in translation, rotation and scale, so that effectively matching between model and image
出处
《通信学报》
EI
CSCD
北大核心
2001年第12期112-116,共5页
Journal on Communications
基金
国防预研基金资助项目
