采用主监视窗扩展技术的增强现实三维注册方法

在增强现实三维注册中,采用重新检测策略时顿卡现象严重。提出一种采用主监视窗扩展技术的增强现实三维注册方法。在检测阶段,引入特征点可信度和主监视窗,并设计主监视窗的分割、扩展和调控策略,大幅提升了计算速度;在跟踪阶段,采用基于图像金字塔的光流算法进行特征点跟踪。实验证明,该方法不仅有效缓解了顿卡现象,也提升了系统整体的实时性。     

Adaptive key SURF feature extraction and application in unmanned vehicle dynamic object recognition

A new method based on adaptive Hessian matrix threshold of finding key SRUF( speeded up robust features) features is proposed and is applied to an unmanned vehicle for its dynamic object recognition and guided navigation. First,the object recognition algorithm based on SURF feature matching for unmanned vehicle guided navigation is introduced. Then,the standard local invariant feature extraction algorithm SRUF is analyzed,the Hessian Metrix is especially discussed,and a method of adaptive Hessian threshold is proposed which is based on correct matching point pairs threshold feedback under a close loop frame. At last,different dynamic object recognition experiments under different weather light conditions are discussed. The experimental result shows that the key SURF feature abstract algorithm and the dynamic object recognition method can be used for unmanned vehicle systems.     

博物馆展示上的多通道投影融合系统

多通道投影融合是现代展览的重要方式和手段。通过在博物馆展示上设计和实现一个投影融合系统,提出并使用新的三角函数形式的融合函数,论述了运用SURF算法进行特征提取,光流法跟踪的动态图像配准处理方法,分析了投影融合主程序的架构流程,阐述了本方法的特点与利弊,并指出了投影融合向3D 发展的方向。在获取SURF 特征点集和投影变换矩阵后,再进行几何校正,边缘融合,色彩平衡的投影融合三步走流程步骤。经过现场采样评价测试,目测和PSNR数据都说明运行融合效果良好。     

Vibration data-driven machine learning architecture for structural health monitoring of steel frame structures

A vibration data-based machine learning architecture is designed for structural health monitoring (SHM) of a steel plane frame structure. This architecture uses a Bag-of-Features algorithm that extracts the speeded-up robust features (SURF) from the time-frequency scalogram images of the registered vibration data. The discriminative image features are then quantised to a visual vocabulary using K-means clustering. Finally, a support vector machine (SVM) is trained to distinguish the undamaged and multiple damage cases of the frame structure based on the discriminative features. The potential of the machine learning architecture is tested for an unseen dataset that was not used in training as well as with some datasets from entirely new damages close to existing (i.e., trained) damage classes. The results are then compared with those obtained using three other combinations of features and learning algorithms—(i) histogram of oriented gradients (HOG) feature with SVM, (ii) SURF feature with k-nearest neighbours (KNN) and (iii) HOG feature with KNN. In order to examine the robustness of the approach, the study is further extended by considering environmental variabilities along with the localisation and quantification of damage. The experimental results show that the machine learning architecture can effectively classify the undamaged and different joint damage classes with high testing accuracy that indicates its SHM potential for such frame structures.     

基于SURF的序列图像快速拼接方法

针对车载摄像平台的序列图像拼接问题,提出一种基于SURF的序列图像快速拼接方法.首先,对经预处理后的序列图像提取SURF特征点,采用最近邻法匹配特征点;其次,利用RANSAC算法估计相邻帧的变换关系;再采用改进的全局配准策略,完成图像配准;最后,采用一种最大值融合法,去除了拼接缝和光照明暗变化的影响,实现序列图像的无缝拼接.实验结果表明该方法适用于车载摄像平台序列图像的快速、无缝拼接,具有较强的鲁棒性,有一定的实用价值.     

基于扩展的SURF描述符的彩色图像配准技术*

目前大多数图像配准算法都需要先将彩色图像转换为灰度图像再进行图像配准,色彩信息的丢失可能引起图像的误匹配。本文在SURF算法的基础上,提出了构建颜色描述向量扩展SURF描述符,形成ESURF描述符,再进行图像配准的方法。该算法能够充分利用彩色图像的色彩信息,相比大多数算法基于灰度图像的配准方法有更高的鲁棒性,同时继承了SURF算法良好的性能。描述符性能测试和图像配准测试证明：ESURF算法比灰度图像SURF算法在图像尺度变化、旋转、模糊、视角变化、特别是光照变化方面有更高的鲁棒性。     

结合SURF描述符和广义近邻图的图像配准算法

针对传统互信息配准方法计算量较大且未利用图像空间信息的缺点,提出了一种结合SURF描述符和广义近邻图的图像配准算法。该算法用SURF从图像中提取尺度空间特征点并获得特征点描述子,然后用广义近邻图来估计Rényi熵与互信息。该算法结合了SURF描述子的鲁棒性和广义近邻图估计Rényi熵的高效性。实验结果表明,对于真实遥感图像,该算法在配准准确度、鲁棒性和速度上都明显优于几种传统配准方法。     

SURF和RANSAC在图像拼接中的应用

图像特征检测在计算机视觉带动下得到了快速发展.SURF特征描述能够非常稳定快速地对图像特征进行检测和描述.RANSAC能够在inliers大于50％的条件下很好地估计出模型参数,在特征点匹配上起到了关键作用.本文利用SURF特征描述子对图像特征点进行检测和描述,然后运用交叉匹配的策略有效地消除一些错误匹配点对,然后运用RANSAC算法进行模型估计,最后使用线性加权的方式对图像进行融合.该方法利用了SURF快速检测和稳定性的特点和RANSAC算法时间复杂度小的特点进行特征点快速准确匹配,最终能够实现快速的图像拼接.