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An efficient image-registration method based on probability density and global parallax

机译:一种基于概率密度和全局视差的有效图像配准方法

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This paper is concerned with the problem of image registration of video sequence, to meet the accuracy and real-time requirements for video analysis and processing. Despite of the existence of sophisticated registration algorithms, it is still problematic to register images precisely with low computation complexity due to the lack of stable features and effective matching method. In this paper, a probability density gradient based interest point detector is employed to extract stable point features precisely. And a robust technique, namely, global parallax histogram based filter is proposed to discard outliers in the initial candidate matches set found by classical correlation method. The registration matrix can then be accurately and precisely estimated using a well adapted criterion. Several field tests are performed on complex and challenging images to assess the performance, including comparison to conventional algorithms, and both inter-frame registration results and statistical analysis of video sequence. These simulations validate the improvement of proposed method in accuracy and efficiency, and the robustness against camera motions, illumination variations, acquirement conditions, moving objects and image noise.
机译:本文关注视频序列的图像配准问题,以满足视频分析和处理的准确性和实时性要求。尽管存在复杂的配准算法,但是由于缺乏稳定的特征和有效的匹配方法,以较低的计算复杂度来精确配准图像仍然存在问题。在本文中,基于概率密度梯度的兴趣点检测器用于精确提取稳定点特征。提出了一种鲁棒的技术,即基于全局视差直方图的滤波器,用于丢弃经典相关方法发现的初始候选匹配集中的离群值。然后可以使用合适的标准来准确而精确地估计配准矩阵。在复杂而具有挑战性的图像上进行了几次现场测试,以评估性能,包括与常规算法的比较以及帧间配准结果和视频序列的统计分析。这些仿真验证了所提方法的准确性和效率以及针对相机运动,照明变化,获取条件,运动物体和图像噪声的鲁棒性的改进。

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