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An IR and visible image sequence automatic registration method based on optical flow

机译:基于光流的红外可见光序列自动配准方法

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摘要

IR–visible camera registration is required for multi-sensor fusion and cooperative processing. Image sequences can provide motion information, which is useful for sequence registration. The existing methods mainly focus on registration using moving objects which are observed by both cameras. However, accurate motion feature extraction for a whole moving object is difficult, because of the complex environment and different imaging mechanism of two sensors. To overcome this problem, we use motion features associated with single pixels in the two image sequences to carry out automatic registration. A normalized optical flow time sequence for each image pixel is constructed. The matching of pixels between the IR image and the visible light image is carried out using a fast similarity measurement and a three stage correspondence selection method. Finally cascaded random sample consensus is adopted to remove outlying matches, and least-square method and Levenberg–Marquardt method are used to estimate the transformation from the IR image to the visible image. The effectiveness of our method is demonstrated using several real datasets and simulated datasets.
机译:多传感器融合和协作处理需要红外可见摄像机配准。图像序列可以提供运动信息,这对于序列配准很有用。现有方法主要集中在使用两个摄像机都观察到的移动物体进行配准。然而,由于复杂的环境和两个传感器的成像机制不同,难以精确地提取整个运动物体的运动特征。为了克服这个问题,我们使用与两个图像序列中的单个像素关联的运动特征来执行自动配准。构造每个图像像素的归一化光流时间序列。 IR图像和可见光图像之间的像素的匹配是使用快速相似性测量和三阶段对应选择方法来进行的。最后,采用级联的随机样本共识来消除外围匹配,并使用最小二乘法和Levenberg-Marquardt方法估计从红外图像到可见图像的转换。使用几个真实的数据集和模拟的数据集证明了我们方法的有效性。

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