Multi-sensor image registration has been widely used in remote sensing and medical image field, but registration performance is degenerated when heterogeneous images are involved. An image registration method based on...Multi-sensor image registration has been widely used in remote sensing and medical image field, but registration performance is degenerated when heterogeneous images are involved. An image registration method based on multi-resolution shape analysis is proposed in this paper, to deal with the problem that the shape of similar objects is always invariant. The contours of shapes are first detected as visual features using an extended contour search algorithm in order to reduce effects of noise, and the multi-resolution shape descriptor is constructed through Fourier curvature representation of the contour’s chain code. Then a minimum distance function is used to judge the similarity between two contours. To avoid the effect of different resolution and intensity distribution, suitable resolution of each image is selected by maximizing the consistency of its pyramid shapes. Finally, the transformation parameters are estimated based on the matched control-point pairs which are the centers of gravity of the closed contours. Multi-sensor Landsat TM imagery and infrared imagery have been used as experimental data for comparison with the classical contour-based registration. Our results have been shown to be superior to the classical ones.展开更多
An automatic image registration approach based on wavelet transform is proposed. This proposed method utilizes multiscale wavelet transform to extract feature points. A coarse-to-fine feature matching method is utiliz...An automatic image registration approach based on wavelet transform is proposed. This proposed method utilizes multiscale wavelet transform to extract feature points. A coarse-to-fine feature matching method is utilized in the feature matching phase. A two-way matching method based on cross-correlation to get candidate point pairs and a fine matching based on support strength combine to form the matching algorithm. At last, based on an affine transformation model, the parameters are iteratively refined by using the leastsquares estimation approach. Experimental results have verified that the proposed algorithm can realize automatic registration of various kinds of images rapidly and effectively.展开更多
In this paper, we proposed a registration method by combining the morphological component analysis(MCA) and scale-invariant feature transform(SIFT) algorithm. This method uses the perception dictionaries,and combines ...In this paper, we proposed a registration method by combining the morphological component analysis(MCA) and scale-invariant feature transform(SIFT) algorithm. This method uses the perception dictionaries,and combines the Basis-Pursuit algorithm and the Total-Variation regularization scheme to extract the cartoon part containing basic geometrical information from the original image, and is stable and unsusceptible to noise interference. Then a smaller number of the distinctive key points will be obtained by using the SIFT algorithm based on the cartoon part of the original image. Matching the key points by the constrained Euclidean distance,we will obtain a more correct and robust matching result. The experimental results show that the geometrical transform parameters inferred by the matched key points based on MCA+SIFT registration method are more exact than the ones based on the direct SIFT algorithm.展开更多
The technique of image matching is the basis of image fusion,image mosaic and scene 3D reconstruction.In the paper a novel image registration method based on SUSAN operator is proposed.Firstly,Corner points are extrac...The technique of image matching is the basis of image fusion,image mosaic and scene 3D reconstruction.In the paper a novel image registration method based on SUSAN operator is proposed.Firstly,Corner points are extracted by using SUSAN(Smallest Univalue Segment Assimilating Nucleus) operator.Then matched corner points are selected through coarse matching and fine matching.Based on such corner pairs,aerophotos are registered automatically.展开更多
基金Project supported by the National Natural Science Foundation of China (No. 60272031), the Hi-Tech Research and Development Program (863) of China (No. 2003AA131032-2), and the Natural Science Foundation of Zhejiang Province (No. M603202), China
文摘Multi-sensor image registration has been widely used in remote sensing and medical image field, but registration performance is degenerated when heterogeneous images are involved. An image registration method based on multi-resolution shape analysis is proposed in this paper, to deal with the problem that the shape of similar objects is always invariant. The contours of shapes are first detected as visual features using an extended contour search algorithm in order to reduce effects of noise, and the multi-resolution shape descriptor is constructed through Fourier curvature representation of the contour’s chain code. Then a minimum distance function is used to judge the similarity between two contours. To avoid the effect of different resolution and intensity distribution, suitable resolution of each image is selected by maximizing the consistency of its pyramid shapes. Finally, the transformation parameters are estimated based on the matched control-point pairs which are the centers of gravity of the closed contours. Multi-sensor Landsat TM imagery and infrared imagery have been used as experimental data for comparison with the classical contour-based registration. Our results have been shown to be superior to the classical ones.
文摘An automatic image registration approach based on wavelet transform is proposed. This proposed method utilizes multiscale wavelet transform to extract feature points. A coarse-to-fine feature matching method is utilized in the feature matching phase. A two-way matching method based on cross-correlation to get candidate point pairs and a fine matching based on support strength combine to form the matching algorithm. At last, based on an affine transformation model, the parameters are iteratively refined by using the leastsquares estimation approach. Experimental results have verified that the proposed algorithm can realize automatic registration of various kinds of images rapidly and effectively.
基金the National Science Foundation of China(No.61471185)the Natural Science Foundation of Shandong Province(No.ZR2016FM21)+1 种基金Shandong Province Science and Technology Plan Project(No.2015GSF116001)Yantai City Key Research and Development Plan Project(Nos.2014ZH157 and2016ZH057)
文摘In this paper, we proposed a registration method by combining the morphological component analysis(MCA) and scale-invariant feature transform(SIFT) algorithm. This method uses the perception dictionaries,and combines the Basis-Pursuit algorithm and the Total-Variation regularization scheme to extract the cartoon part containing basic geometrical information from the original image, and is stable and unsusceptible to noise interference. Then a smaller number of the distinctive key points will be obtained by using the SIFT algorithm based on the cartoon part of the original image. Matching the key points by the constrained Euclidean distance,we will obtain a more correct and robust matching result. The experimental results show that the geometrical transform parameters inferred by the matched key points based on MCA+SIFT registration method are more exact than the ones based on the direct SIFT algorithm.
文摘The technique of image matching is the basis of image fusion,image mosaic and scene 3D reconstruction.In the paper a novel image registration method based on SUSAN operator is proposed.Firstly,Corner points are extracted by using SUSAN(Smallest Univalue Segment Assimilating Nucleus) operator.Then matched corner points are selected through coarse matching and fine matching.Based on such corner pairs,aerophotos are registered automatically.
文摘异源图像配准中,由于图像的成像机理差异,图像像素强度关联和旋转畸变是不可避免的两大问题,针对图像像素强度关联问题,提出了基于辐射不变特征变换(radiation-variation insensitive feature transform,RIFT)的图像配准算法,对图像间像素关联差异小的图像对配准有良好的精度,但对旋转畸变图像会产生较多错误匹配。对于旋转畸变问题,传统的ORB(oriented fast and rotated brief)算法,对旋转图像的配准有一定的稳定性,但对于强度变化不明显的图像对,特征点检测质量较低,配准精度不理想。因此本文将相位一致性(phase consistency,PC)融合进ORB算法,利用相位信息代替传统的图像强度信息,再构造旋转不变性BRIEF特征描述子,对图像像素强度变化和旋转畸变均具有鲁棒性。用图像像素强度关联不明显的红外图像和可见光图像进行配准实验,本文算法针对不同旋转幅度的图像的配准精度较高,RMSE稳定在1.7~2.1,优于RIFT算法,在特征点检测数量、配准精度和效率等性能上均有良好性能。
