A binocular stereo vision positioning method based on the scale-invariant feature trans- form （SIFT） algorithm is proposed. The SIFT algorithm is for extracting distinctive invariant features from images. First, ima...A binocular stereo vision positioning method based on the scale-invariant feature trans- form （SIFT） algorithm is proposed. The SIFT algorithm is for extracting distinctive invariant features from images. First, image median filtering is used to eliminate image noise. Then, according to the characteristics of the target satellite, image map is used to extract the middle part of the target satel- lite. At last, the feature match point under the SIFT algorithm is extracted, and the three-dimension- al position and orientation are calculated. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The experimental result shows that the al- gorithm works well and the maximum relative error is within 0. 02 m and 2.5 o展开更多
A novel algorithm is presented to make the results of image matching more reliable and accurate based on SIFT (Scale Invariant Feature Transform). SIFT algorithm has been identified as the most resistant matching algo...A novel algorithm is presented to make the results of image matching more reliable and accurate based on SIFT (Scale Invariant Feature Transform). SIFT algorithm has been identified as the most resistant matching algorithm to common image deformations; however, if there are similar regions in images, SIFT algorithm still generates some analogical descriptors and provides many mismatches. This paper examines the local image descriptor used by SIFT and presents a new algorithm by integrating SIFT with two-dimensional moment invariants and disparity gradient to improve the matching results. In the new algorithm, decision tree is used, and the whole matching process is divided into three levels with different primitives. Matching points are considered as correct ones only when they satisfy all the three similarity measurements. Experiment results demonstrate that the new approach is more reliable and accurate.展开更多
The paper analyze and improve the SIFT optimized algorithm, and proposes an image matching method for SIFT algorithm based on quasi Euclidean distance and KD-tree. Experiments show that this algorithm has matching mor...The paper analyze and improve the SIFT optimized algorithm, and proposes an image matching method for SIFT algorithm based on quasi Euclidean distance and KD-tree. Experiments show that this algorithm has matching more points, high matching accuracy, no repealed points and higher advantage of matching efficiency based on keeping the basic characteristics of SIFT algorithm unchanged, and provides precise matching point to generate precise image stitching and other related fields of the follow-up product. At the same time, this method was applied to the layout optimization and achieved good results.展开更多
In this article, an appropriate strategy for registration of correspondent points in the stereo-pairs of Chang’E-1 lunar mission has been introduced. It consists of area-based method and feature-based method as two s...In this article, an appropriate strategy for registration of correspondent points in the stereo-pairs of Chang’E-1 lunar mission has been introduced. It consists of area-based method and feature-based method as two steps. Firstly, one subimage was extracted from nadir image as reference image. Making use of area-based method, another subimage which is called target image can be obtained from backward or forward image overlapping the same region of lunar surface with reference image. Secondly, feature points of each subimage can be extracted by SIFT （scale invariant feature transform） algorithm. Lastly, for each feature point given in reference image, the position of correspondence in target image can be estimated according to the parameters of Chang’E-1 lunar orbiter. In contrast to standard SIFT matching algorithm, the method proposed in this article can narrow the search space and accelerate the speed of computation while achieving reduction of the percentage of false registration.展开更多
文摘A binocular stereo vision positioning method based on the scale-invariant feature trans- form （SIFT） algorithm is proposed. The SIFT algorithm is for extracting distinctive invariant features from images. First, image median filtering is used to eliminate image noise. Then, according to the characteristics of the target satellite, image map is used to extract the middle part of the target satel- lite. At last, the feature match point under the SIFT algorithm is extracted, and the three-dimension- al position and orientation are calculated. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The experimental result shows that the al- gorithm works well and the maximum relative error is within 0. 02 m and 2.5 o
文摘A novel algorithm is presented to make the results of image matching more reliable and accurate based on SIFT (Scale Invariant Feature Transform). SIFT algorithm has been identified as the most resistant matching algorithm to common image deformations; however, if there are similar regions in images, SIFT algorithm still generates some analogical descriptors and provides many mismatches. This paper examines the local image descriptor used by SIFT and presents a new algorithm by integrating SIFT with two-dimensional moment invariants and disparity gradient to improve the matching results. In the new algorithm, decision tree is used, and the whole matching process is divided into three levels with different primitives. Matching points are considered as correct ones only when they satisfy all the three similarity measurements. Experiment results demonstrate that the new approach is more reliable and accurate.
文摘The paper analyze and improve the SIFT optimized algorithm, and proposes an image matching method for SIFT algorithm based on quasi Euclidean distance and KD-tree. Experiments show that this algorithm has matching more points, high matching accuracy, no repealed points and higher advantage of matching efficiency based on keeping the basic characteristics of SIFT algorithm unchanged, and provides precise matching point to generate precise image stitching and other related fields of the follow-up product. At the same time, this method was applied to the layout optimization and achieved good results.
基金supported by the Science and Technology Development Fund of Macao(Nos.004/2011/A1)the National Natural Science Fundation of China(No.61272364)
文摘In this article, an appropriate strategy for registration of correspondent points in the stereo-pairs of Chang’E-1 lunar mission has been introduced. It consists of area-based method and feature-based method as two steps. Firstly, one subimage was extracted from nadir image as reference image. Making use of area-based method, another subimage which is called target image can be obtained from backward or forward image overlapping the same region of lunar surface with reference image. Secondly, feature points of each subimage can be extracted by SIFT （scale invariant feature transform） algorithm. Lastly, for each feature point given in reference image, the position of correspondence in target image can be estimated according to the parameters of Chang’E-1 lunar orbiter. In contrast to standard SIFT matching algorithm, the method proposed in this article can narrow the search space and accelerate the speed of computation while achieving reduction of the percentage of false registration.
