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SIFT Flow: Dense Correspondence across Scenes and Its Applications

机译：SIFT流：跨场景的密集对应关系及其应用

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摘要

While image alignment has been studied in different areas of computer vision for decades, aligning images depicting different scenes remains a challenging problem. Analogous to optical flow, where an image is aligned to its temporally adjacent frame, we propose SIFT flow, a method to align an image to its nearest neighbors in a large image corpus containing a variety of scenes. The SIFT flow algorithm consists of matching densely sampled, pixelwise SIFT features between two images while preserving spatial discontinuities. The SIFT features allow robust matching across different scene/object appearances, whereas the discontinuity-preserving spatial model allows matching of objects located at different parts of the scene. Experiments show that the proposed approach robustly aligns complex scene pairs containing significant spatial differences. Based on SIFT flow, we propose an alignment-based large database framework for image analysis and synthesis, where image information is transferred from the nearest neighbors to a query image according to the dense scene correspondence. This framework is demonstrated through concrete applications such as motion field prediction from a single image, motion synthesis via object transfer, satellite image registration, and face recognition.
机译：尽管数十年来一直在计算机视觉的不同领域研究图像对齐方式，但是对齐表示不同场景的图像仍然是一个具有挑战性的问题。类似于光流，其中图像与时间上相邻的帧对齐，我们提出了SIFT流，这是一种将图像与包含各种场景的大型图像库中的图像最接近的邻居对齐的方法。 SIFT流算法包括在两个图像之间匹配密集采样的逐像素SIFT特征，同时保留空间不连续性。 SIFT功能允许跨不同场景/对象外观进行鲁棒匹配，而保留不连续性的空间模型允许匹配位于场景不同部分的对象。实验表明，所提出的方法可以鲁棒地对齐包含明显空间差异的复杂场景对。基于SIFT流，我们提出了一种基于对齐的大型数据库框架，用于图像分析和合成，其中图像信息根据密集场景的对应关系从最近的邻居传输到查询图像。该框架通过具体应用进行了演示，例如从单个图像进行运动场预测，通过对象传输进行运动合成，卫星图像配准和面部识别。

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