We propose Iterative Homography Network, namely IHN, a new deep homography
estimation architecture. Different from previous works that achieve iterative
refinement by network cascading or untrainable IC-LK iterator, the iterator of
IHN has tied weights and is completely trainable. IHN achieves state-of-the-art
accuracy on several datasets including challenging scenes. We propose 2
versions of IHN: (1) IHN for static scenes, (2) IHN-mov for dynamic scenes with
moving objects. Both versions can be arranged in 1-scale for efficiency or
2-scale for accuracy. We show that the basic 1-scale IHN already outperforms
most of the existing methods. On a variety of datasets, the 2-scale IHN
outperforms all competitors by a large gap. We introduce IHN-mov by producing
an inlier mask to further improve the estimation accuracy of moving-objects
scenes. We experimentally show that the iterative framework of IHN can achieve
95% error reduction while considerably saving network parameters. When
processing sequential image pairs, IHN can achieve 32.7 fps, which is about 8x
the speed of IC-LK iterator. Source code is available at
this https URL

本文提出了迭代单应性网络（IHN），一种新的深度学习单应性估计架构，通过可训练的迭代器和多尺度方法可以实现对静态场景和动态场景中移动目标的高精度估计。实验表明，IHN 可以实现 95％的误差减少和 32.7fps 的处理速度。

迭代深度单应矩阵估计

Iterative Deep Homography Estimation

In this study, we develop an unsupervised coarse-to-fine video analysis
framework and prototype system to extract a salient object in a video sequence.
This framework starts from tracking grid-sampled points along temporal frames,
typically using KLT tracking method. The tracking points could be divided into
several groups due to their inconsistent movements. At the same time, the SLIC
algorithm is extended into 3D space to generate supervoxels. Coarse
segmentation is achieved by combining the categorized tracking points and
supervoxels of the corresponding frame in the video sequence. Finally, a
graph-based fine segmentation algorithm is used to extract the moving object in
the scene. Experimental results reveal that this method outperforms the
previous approaches in terms of accuracy and robustness.

本研究提出了一种无监督的视频分析框架和原型系统来提取视频序列中的显著对象，其通过跟踪时间帧上的采样点，并结合 3D 空间中的超像素，将视频分为粗分割和细分割两步来实现，实验结果表明该方法在准确性和鲁棒性方面优于之前的方法。