A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low freq...A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix （or matching result） reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.展开更多
Augmented solar images were used to research the adaptability of four representative image extraction and matching algorithms in space weather domain.These include the scale-invariant feature transform algorithm,speed...Augmented solar images were used to research the adaptability of four representative image extraction and matching algorithms in space weather domain.These include the scale-invariant feature transform algorithm,speeded-up robust features algorithm,binary robust invariant scalable keypoints algorithm,and oriented fast and rotated brief algorithm.The performance of these algorithms was estimated in terms of matching accuracy,feature point richness,and running time.The experiment result showed that no algorithm achieved high accuracy while keeping low running time,and all algorithms are not suitable for image feature extraction and matching of augmented solar images.To solve this problem,an improved method was proposed by using two-frame matching to utilize the accuracy advantage of the scale-invariant feature transform algorithm and the speed advantage of the oriented fast and rotated brief algorithm.Furthermore,our method and the four representative algorithms were applied to augmented solar images.Our application experiments proved that our method achieved a similar high recognition rate to the scale-invariant feature transform algorithm which is significantly higher than other algorithms.Our method also obtained a similar low running time to the oriented fast and rotated brief algorithm,which is significantly lower than other algorithms.展开更多
【目的】煤矿井下普遍存在低照度、弱纹理和结构化的特征退化场景,导致视觉SLAM(visual simultaneous localization and mapping)系统面临有效特征不足或误匹配率高的问题,严重制约了其定位的准确性和鲁棒性。【方法】提出一种基于边缘...【目的】煤矿井下普遍存在低照度、弱纹理和结构化的特征退化场景,导致视觉SLAM(visual simultaneous localization and mapping)系统面临有效特征不足或误匹配率高的问题,严重制约了其定位的准确性和鲁棒性。【方法】提出一种基于边缘感知增强的视觉SLAM方法。首先,构建了边缘感知约束的低光图像增强模块。通过自适应尺度的梯度域引导滤波器优化Retinex算法,以获得纹理清晰光照均匀的图像,从而显著提升了在低光照和不均匀光照条件下特征提取性能。其次,在视觉里程计中构建了边缘感知增强的特征提取和匹配模块,通过点线特征融合策略有效增强了弱纹理和结构化场景中特征的可检测性和匹配准确性。具体使用边缘绘制线特征提取算法(edge drawing lines,EDLines)提取线特征,定向FAST和旋转BRIEF点特征提取算法(oriented fast and rotated brief,ORB)提取点特征,并利用基于网格运动统计(grid-based motion statistics,GMS)和比值测试匹配算法进行精确匹配。最后,将该方法与ORB-SLAM2、ORB-SLAM3在TUM数据集和煤矿井下实景数据集上进行了全面实验验证,涵盖图像增强、特征匹配和定位等多个环节。【结果和结论】结果表明:(1)在TUM数据集上的测试结果显示,所提方法与ORB-SLAM2相比,绝对轨迹误差、相对轨迹误差的均方根误差分别降低了4%~38.46%、8.62%~50%;与ORB-SLAM3相比,绝对轨迹误差、相对轨迹误差的均方根误差分别降低了0~61.68%、3.63%~47.05%。(2)在煤矿井下实景实验中,所提方法的定位轨迹更接近于相机运动参考轨迹。(3)有效提高了视觉SLAM在煤矿井下特征退化场景中的准确性和鲁棒性,为视觉SLAM技术在煤矿井下的应用提供了技术解决方案。研究面向井下特征退化场景的视觉SLAM方法,对于推动煤矿井下移动式装备机器人化具有重要意义。展开更多
基金supported by the National Natural Science Foundation of China (6117212711071002)+1 种基金the Specialized Research Fund for the Doctoral Program of Higher Education (20113401110006)the Innovative Research Team of 211 Project in Anhui University (KJTD007A)
文摘A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix （or matching result） reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.
基金Supported by the Key Research Program of the Chinese Academy of Sciences(ZDRE-KT-2021-3)。
文摘Augmented solar images were used to research the adaptability of four representative image extraction and matching algorithms in space weather domain.These include the scale-invariant feature transform algorithm,speeded-up robust features algorithm,binary robust invariant scalable keypoints algorithm,and oriented fast and rotated brief algorithm.The performance of these algorithms was estimated in terms of matching accuracy,feature point richness,and running time.The experiment result showed that no algorithm achieved high accuracy while keeping low running time,and all algorithms are not suitable for image feature extraction and matching of augmented solar images.To solve this problem,an improved method was proposed by using two-frame matching to utilize the accuracy advantage of the scale-invariant feature transform algorithm and the speed advantage of the oriented fast and rotated brief algorithm.Furthermore,our method and the four representative algorithms were applied to augmented solar images.Our application experiments proved that our method achieved a similar high recognition rate to the scale-invariant feature transform algorithm which is significantly higher than other algorithms.Our method also obtained a similar low running time to the oriented fast and rotated brief algorithm,which is significantly lower than other algorithms.
文摘【目的】煤矿井下普遍存在低照度、弱纹理和结构化的特征退化场景,导致视觉SLAM(visual simultaneous localization and mapping)系统面临有效特征不足或误匹配率高的问题,严重制约了其定位的准确性和鲁棒性。【方法】提出一种基于边缘感知增强的视觉SLAM方法。首先,构建了边缘感知约束的低光图像增强模块。通过自适应尺度的梯度域引导滤波器优化Retinex算法,以获得纹理清晰光照均匀的图像,从而显著提升了在低光照和不均匀光照条件下特征提取性能。其次,在视觉里程计中构建了边缘感知增强的特征提取和匹配模块,通过点线特征融合策略有效增强了弱纹理和结构化场景中特征的可检测性和匹配准确性。具体使用边缘绘制线特征提取算法(edge drawing lines,EDLines)提取线特征,定向FAST和旋转BRIEF点特征提取算法(oriented fast and rotated brief,ORB)提取点特征,并利用基于网格运动统计(grid-based motion statistics,GMS)和比值测试匹配算法进行精确匹配。最后,将该方法与ORB-SLAM2、ORB-SLAM3在TUM数据集和煤矿井下实景数据集上进行了全面实验验证,涵盖图像增强、特征匹配和定位等多个环节。【结果和结论】结果表明:(1)在TUM数据集上的测试结果显示,所提方法与ORB-SLAM2相比,绝对轨迹误差、相对轨迹误差的均方根误差分别降低了4%~38.46%、8.62%~50%;与ORB-SLAM3相比,绝对轨迹误差、相对轨迹误差的均方根误差分别降低了0~61.68%、3.63%~47.05%。(2)在煤矿井下实景实验中,所提方法的定位轨迹更接近于相机运动参考轨迹。(3)有效提高了视觉SLAM在煤矿井下特征退化场景中的准确性和鲁棒性,为视觉SLAM技术在煤矿井下的应用提供了技术解决方案。研究面向井下特征退化场景的视觉SLAM方法,对于推动煤矿井下移动式装备机器人化具有重要意义。
