针对图像序列的快速低存储开销配准算法

针对序列图像配准问题，提出一种快速低存储开销配准算法。首先，生成一系列与具体图像内容无关的特征点。而后，使用正向-反向跟踪来获取稳定的特征点对,其中，正向跟踪用于获得所有可能的特征点对，反向跟踪用来得到正向-反向误差，并且利用此误差来获取最终稳定的特征点对。最后，在稳定特征点对的基础上通过归一化直接线性变换计算得到可用于图像配准的单应矩阵。实验表明该算法能够提供与优秀的传统算法相当的配准性能。由于该算法对序列中图像之间的连续性进行了充分利用，不仅降低了存储开销，还提高了运算速度。对480360的序列而言，该算法需要的存储开销仅为421 kB，且运算速度达到32 帧/s。

A fast and memory-saving algorithm for sequence-based image registration

A fast and memory-saving registration algorithm is proposed for image sequence-based computer vision applications. First, a new scheme, which is independent of the image content, is adopted to generate a series of feature points. Then, a forward-and-backward tracking approach is used to obtain the reliable feature point pairs. In the approach, the forward tracking is performed to get all the potential feature point pairs, and the backward tracking is utilized to measure the forward-backward discrepancies which enable selection of the final reliable point pairs. Finally, building upon these reliable pairs, the homography is computed by using normalized direct linear transformation. Due to the intensive exploitation of the interframe continuity existing in image sequences, the proposed registration algorithm provides comparable experimental results to current state-of-the-art techniques, while using a fraction of the computation time and a fraction of the memory as well. Specifically, with a memory usage of 421 kB only, this algorithm runs at 32 frames per second for a sequence with an image resolution of 480×360.