ORB(Oriented FAST and rotated BRIEF)特征描述算法具有旋转不变性、匹配速度快的特点,但没有解决尺度不变性、误匹配率高的问题。针对此缺陷,提出一种改进的ORB特征点匹配算法,完成特征点的检测、匹配以及剔除误匹配。改进算法首先借...ORB(Oriented FAST and rotated BRIEF)特征描述算法具有旋转不变性、匹配速度快的特点,但没有解决尺度不变性、误匹配率高的问题。针对此缺陷,提出一种改进的ORB特征点匹配算法,完成特征点的检测、匹配以及剔除误匹配。改进算法首先借鉴了A-KAZE基于非线性扩散滤波构建尺度空间的方法;其次利用ORB特征检测子在所构建的非线性尺度空间进行特征点的检测;再次对采集到的特征点生成特征描述子;最后在使用Hamming距离匹配的基础上再对其结果采用PROSAC算法剔除噪声点。实验结果表明,改进后的算法相较于原ORB算法,有效地解决了ORB算法不具备尺度不变性的问题,且匹配精度大幅提高,适用于尺度变化较大且实时性高的环境,具有较好的工程意义。展开更多
针对目前变电站运动目标的立体匹配算法存在匹配点少、误匹配等问题,提出一种结合A-KAZE(Accelerated KAZE)算法和改进的SURF(Speeded Up Robust Features)算法的智能变电站运动目标立体匹配算法。采用A-KAZE算法用于提取两个图像的匹...针对目前变电站运动目标的立体匹配算法存在匹配点少、误匹配等问题,提出一种结合A-KAZE(Accelerated KAZE)算法和改进的SURF(Speeded Up Robust Features)算法的智能变电站运动目标立体匹配算法。采用A-KAZE算法用于提取两个图像的匹配特征点,利用二阶多尺度改进的SURF特征向量进一步计算二次响应,采用高阈值算法增加匹配点,随机采样一致算法消除不匹配点,完成匹配工作。通过实验比较,验证了该算法的有效性。实验结果表明,相对于未改进前匹配点对从908对提高到1202对,匹配准确率从92.51%提高到96.17%,具有一定的实用价值。展开更多
At present,underwater terrain images are all strip-shaped small fragment images preprocessed by the side-scan sonar imaging system.However,the processed underwater terrain images have inconspicuous and few feature poi...At present,underwater terrain images are all strip-shaped small fragment images preprocessed by the side-scan sonar imaging system.However,the processed underwater terrain images have inconspicuous and few feature points.In order to better realize the stitching of underwater terrain images and solve the problems of slow traditional image stitching speed,we proposed an improved algorithm for underwater terrain image stitching based on spatial gradient feature block.First,the spatial gradient fuzzy C-Means algorithm is used to divide the underwater terrain image into feature blocks with the fusion of spatial gradient information.The accelerated-KAZE(AKAZE)algorithm is used to combine the feature block information to match the reference image and the target image.Then,the random sample consensus(RANSAC)is applied to optimize the matching results.Finally,image fusion is performed with the global homography and the optimal seam-line method to improve the accuracy of image overlay fusion.The experimental results show that the proposed method in this paper effectively divides images into feature blocks by combining spatial information and gradient information,which not only solves the problem of stitching failure of underwater terrain images due to unobvious features,and further reduces the sensitivity to noise,but also effectively reduces the iterative calculation in the feature point matching process of the traditional method,and improves the stitching speed.Ghosting and shape warping are significantly eliminated by re-optimizing the overlap of the image.展开更多
文摘ORB(Oriented FAST and rotated BRIEF)特征描述算法具有旋转不变性、匹配速度快的特点,但没有解决尺度不变性、误匹配率高的问题。针对此缺陷,提出一种改进的ORB特征点匹配算法,完成特征点的检测、匹配以及剔除误匹配。改进算法首先借鉴了A-KAZE基于非线性扩散滤波构建尺度空间的方法;其次利用ORB特征检测子在所构建的非线性尺度空间进行特征点的检测;再次对采集到的特征点生成特征描述子;最后在使用Hamming距离匹配的基础上再对其结果采用PROSAC算法剔除噪声点。实验结果表明,改进后的算法相较于原ORB算法,有效地解决了ORB算法不具备尺度不变性的问题,且匹配精度大幅提高,适用于尺度变化较大且实时性高的环境,具有较好的工程意义。
文摘针对目前变电站运动目标的立体匹配算法存在匹配点少、误匹配等问题,提出一种结合A-KAZE(Accelerated KAZE)算法和改进的SURF(Speeded Up Robust Features)算法的智能变电站运动目标立体匹配算法。采用A-KAZE算法用于提取两个图像的匹配特征点,利用二阶多尺度改进的SURF特征向量进一步计算二次响应,采用高阈值算法增加匹配点,随机采样一致算法消除不匹配点,完成匹配工作。通过实验比较,验证了该算法的有效性。实验结果表明,相对于未改进前匹配点对从908对提高到1202对,匹配准确率从92.51%提高到96.17%,具有一定的实用价值。
基金This research was funded by College Student Innovation and Entrepreneurship Training Program,Grant Number 2021055Z and S202110082031the Special Project for Cultivating Scientific and Technological Innovation Ability of College and Middle School Students in Hebei Province,Grant Number 2021H011404.
文摘At present,underwater terrain images are all strip-shaped small fragment images preprocessed by the side-scan sonar imaging system.However,the processed underwater terrain images have inconspicuous and few feature points.In order to better realize the stitching of underwater terrain images and solve the problems of slow traditional image stitching speed,we proposed an improved algorithm for underwater terrain image stitching based on spatial gradient feature block.First,the spatial gradient fuzzy C-Means algorithm is used to divide the underwater terrain image into feature blocks with the fusion of spatial gradient information.The accelerated-KAZE(AKAZE)algorithm is used to combine the feature block information to match the reference image and the target image.Then,the random sample consensus(RANSAC)is applied to optimize the matching results.Finally,image fusion is performed with the global homography and the optimal seam-line method to improve the accuracy of image overlay fusion.The experimental results show that the proposed method in this paper effectively divides images into feature blocks by combining spatial information and gradient information,which not only solves the problem of stitching failure of underwater terrain images due to unobvious features,and further reduces the sensitivity to noise,but also effectively reduces the iterative calculation in the feature point matching process of the traditional method,and improves the stitching speed.Ghosting and shape warping are significantly eliminated by re-optimizing the overlap of the image.
