In this paper we address the problem of matching two images with two different resolutions: a high-resolution image and a low-resolution one. The difference in resolution between the two images is not known and without loss of generality one of the images is assumed to be the high-resolution one. On the premise that changes in resolution act as a smoothing equivalent to changes in scale, a scale-space representation of the high-resolution image is produced. Hence the one-to-one classical image matching paradigm becomes one-to-many because the low-resolution image is compared with all the scale-space representations of the high-resolution one. Key to the success of such a process is the proper representation of the features to be matched in scale-space. We show how to represent and extract interest points at variable scales and we devise a method allowing the comparison of two images at two different resolutions. The method comprises the use of photometric- and rotation-invariant descriptors, a geometric model mapping the high-resolution image onto a low-resolution image region, and an image matching strategy based on local constraints and on the robust estimation of this geometric model. Extensive experiments show that our matching method can be used for scale changes up to a factor of 6.
翻译:在本文中,我们处理将两个图像与两个不同分辨率相匹配的问题:高分辨率图像和低分辨率图像。两种图像的分辨率差异不为人知,而且不失一般性。两种图像的分辨率差异被假定为高分辨率图像之一。在分辨率变化作为平滑与比例变化相当的前提下,生成了高分辨率图像的尺度空间表示法。因此,一对一的经典图像匹配模式成为一对一的典型模式,因为低分辨率图像与高分辨率图像的所有比例-空间表示法进行了比较。这一过程成功的关键在于如何适当体现在比例空间上匹配的特征。我们展示了如何在变量尺度上代表并提取利益点,我们设计了一种方法,允许在两个不同的分辨率上对两种图像进行比较。这种方法包括使用光度测量和旋转性内变量描述器,一种测量模型将高分辨率图像映射到低分辨率图像区域,以及一种基于当地制约因素和对这个测量模型进行可靠估计的图像匹配战略。大规模实验显示,我们使用的比对比例值方法可以用来进行比值。