Image Engineering & Video Technology Lab, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.
Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China, Beijing 100081, China.
Sensors (Basel). 2019 Feb 26;19(5):997. doi: 10.3390/s19050997.
To solve the problems of the large differences in gray value and inaccurate positioning of feature information during infrared-visible image registration, we propose an automatic and robust algorithm for registering planar infrared-visible image sequences through spatio-temporal association. In particular, we first create motion vector distribution descriptors which represent the temporal motion information of foreground contours in adjacent frames to complete coarse registration without feature extraction. Then, for precise registration, we extracted FAST corners of the foreground, which are described by the spatial location distribution of contour points based on connected blob detection, and match these corners using bidirectional optimal maximum strategy. Finally, a reservoir updated by Better-In, Worse-Out (BIWO) strategy is established to save matched point pairs and obtain the optimal global transformation matrix. Extensive evaluations on the LITIV dataset well demonstrate the effectiveness of the proposed algorithm. Particularly, our algorithm achieves lower registration overlapping errors than the other two state-of-the-arts.
为了解决红外-可见光图像配准过程中灰度值差异大、特征信息定位不准确的问题，我们提出了一种基于时空关联的平面红外-可见光图像序列自动、鲁棒的配准算法。具体来说，我们首先创建运动矢量分布描述符，它表示相邻帧中前景轮廓的时间运动信息，以完成无需特征提取的粗配准。然后，为了进行精确配准，我们提取了前景的 FAST 角点，这些角点是基于连通blob 检测的轮廓点的空间位置分布来描述的，并使用双向最优最大策略来匹配这些角点。最后，建立了一个由 Better-In，Worse-Out (BIWO) 策略更新的蓄水池，以保存匹配的点对，并获得最优的全局变换矩阵。在 LITIV 数据集上的广泛评估表明，所提出的算法是有效的。特别是，我们的算法的注册重叠误差比其他两种方法都要低。
