模式识别与人工智能
模式識彆與人工智能
모식식별여인공지능
Moshi Shibie yu Rengong Zhineng
2015年
8期
694-701
,共8页
大尺度图像配准%归一化梯度相位相关%参数估计
大呎度圖像配準%歸一化梯度相位相關%參數估計
대척도도상배준%귀일화제도상위상관%삼수고계
Large﹣Scale Image Registration%Normalized Gradient Phase Correlation%Parameter Estimation
针对具有旋转、缩放、平移的大尺度变换图像的实时配准问题，提出基于归一化梯度相位相关的图像配准算法。该算法避免复杂的多层插值计算和迭代处理过程，利用归一化梯度相位相关法处理复数梯度图像，能在兼顾参数估计的鲁棒性和快速性的同时，扩大图像变换参数的估计范围。并通过一种参数可调整的窗函数有效抑制不同种类图像的边缘效应的影响。实验证明该算法的快速性和有效性。
針對具有鏇轉、縮放、平移的大呎度變換圖像的實時配準問題，提齣基于歸一化梯度相位相關的圖像配準算法。該算法避免複雜的多層插值計算和迭代處理過程，利用歸一化梯度相位相關法處理複數梯度圖像，能在兼顧參數估計的魯棒性和快速性的同時，擴大圖像變換參數的估計範圍。併通過一種參數可調整的窗函數有效抑製不同種類圖像的邊緣效應的影響。實驗證明該算法的快速性和有效性。
침대구유선전、축방、평이적대척도변환도상적실시배준문제，제출기우귀일화제도상위상관적도상배준산법。해산법피면복잡적다층삽치계산화질대처리과정，이용귀일화제도상위상관법처리복수제도도상，능재겸고삼수고계적로봉성화쾌속성적동시，확대도상변환삼수적고계범위。병통과일충삼수가조정적창함수유효억제불동충류도상적변연효응적영향。실험증명해산법적쾌속성화유효성。
To solve the real﹣time registration problem of large﹣scale images with rotations, scalings, translations simultaneously, an image registration algorithm based on normalized gradient phase correlation is proposed in this paper. The complicated multilayer computation, interpolation and iteration is avoided in this algorithm. Plural gradient images are disposed by normalized gradient phase correlation. Giving consideration to robustness and rapidity of parameters estimation at the same time, this algorithm can efficiently expand the estimation range of transformation parameters. By means of parameter﹣adjustable window function, it can suppress the influence of the edge effect of the different kinds of images. Experimental results illustrate the rapidity and effectiveness of the proposed algorithm.
