Hausdorff distance measure is one of the widely adopted feature-based image matching algo- rithms due to its simplicity and accuracy. However, it is considered that its robustness still needs to be improved. In this p...Hausdorff distance measure is one of the widely adopted feature-based image matching algo- rithms due to its simplicity and accuracy. However, it is considered that its robustness still needs to be improved. In this paper, various forms of original and improved Hausdorff distance （HD） and their limitations are studied. Focusing on robust Hausdorff distance （ RHD）, an improved RHD with an adaptive outlier point threshold selection method is proposed. Furthermore, another new form of the Hausdorff distance which possesses the merits of RHD and M-HD is prsented. Finally, a recur- sire algorithm is introduced to accelerate the image matching speed of Hausdorff algorithms. Exten- sive simulation and experiment results are presented to validate the feasibility of the proposed Haus- dorff distance algorithm.展开更多
The mean Hausdorff distance, though highly applicable in image registration, does not work well on partial matching images. An improvement upon traditional Hausdorff-distance-based image registration method is propose...The mean Hausdorff distance, though highly applicable in image registration, does not work well on partial matching images. An improvement upon traditional Hausdorff-distance-based image registration method is proposed, which consists of the following two aspects. One is to estimate transformation parameters between two images from the distributions of geometric property differences instead of establishing explicit feature correspondences. This procedure is treated as the pre-registration. The other aspect is that mean Hausdorff distance computation is replaced with the analysis of the second difference of generalized Hausdorff distance so as to eliminate the redundant points. Experimental results show that our registration method outperforms the method based on mean Hausdorff distance. The registration errors are noticeably reduced in the partial matching images.展开更多
Object matching between two-dimensional images is an important problem in computer vision. The purpose of object matching is to decide the similarity between two objects. A new robust image matching method based on di...Object matching between two-dimensional images is an important problem in computer vision. The purpose of object matching is to decide the similarity between two objects. A new robust image matching method based on distance reciprocal was presented. The distance reciprocal is based on human visual perception. This method is simple and effective. Moreover, it is robust against noise. The experiments show that this method outperforms the Hausdorff distance, when the images with noise interfered need to be recognized.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
基金Supported by the National Natural Science Foundation of China(No.61072088)
文摘Hausdorff distance measure is one of the widely adopted feature-based image matching algo- rithms due to its simplicity and accuracy. However, it is considered that its robustness still needs to be improved. In this paper, various forms of original and improved Hausdorff distance （HD） and their limitations are studied. Focusing on robust Hausdorff distance （ RHD）, an improved RHD with an adaptive outlier point threshold selection method is proposed. Furthermore, another new form of the Hausdorff distance which possesses the merits of RHD and M-HD is prsented. Finally, a recur- sire algorithm is introduced to accelerate the image matching speed of Hausdorff algorithms. Exten- sive simulation and experiment results are presented to validate the feasibility of the proposed Haus- dorff distance algorithm.
基金Project(61070090)supported by the National Natural Science Foundation of ChinaProject(2012J4300030)supported by the GuangzhouScience and Technology Support Key Projects,China
文摘The mean Hausdorff distance, though highly applicable in image registration, does not work well on partial matching images. An improvement upon traditional Hausdorff-distance-based image registration method is proposed, which consists of the following two aspects. One is to estimate transformation parameters between two images from the distributions of geometric property differences instead of establishing explicit feature correspondences. This procedure is treated as the pre-registration. The other aspect is that mean Hausdorff distance computation is replaced with the analysis of the second difference of generalized Hausdorff distance so as to eliminate the redundant points. Experimental results show that our registration method outperforms the method based on mean Hausdorff distance. The registration errors are noticeably reduced in the partial matching images.
文摘Object matching between two-dimensional images is an important problem in computer vision. The purpose of object matching is to decide the similarity between two objects. A new robust image matching method based on distance reciprocal was presented. The distance reciprocal is based on human visual perception. This method is simple and effective. Moreover, it is robust against noise. The experiments show that this method outperforms the Hausdorff distance, when the images with noise interfered need to be recognized.
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.
