Image alignment is one of the first steps for most computer vision and image processing algorithms. Image fusion, image mosaicing, creation of panoramas, object recognition/detection, photometric stereo and enhanced rendering are some of the examples in which image alignment is a crucial step. In this work, we focus on alignment of high-resolution images taken with a fixed camera under different light directions. Although the camera position is largely fixed, there might be some misalignment due to perturbations to the camera or to the object, or the effect of optical image stabilization, especially in long photo shoots. Based on our experiments, we observe that feature-based techniques outperform pixel-based ones for this application. We found that SIFT [Low04] and SURF [BTVG06] provided very reliable features for most cases. For feature-based approaches, one of the main problems is the elimination of outliers, and we solve this problem using the RANSAC framework. Furthermore, we propose a method to automatically detect the transformation model between images. The datasets that we focus on have around 10-100 images, of the same scene, and in order to take advantage of having many images, we explore a graph-based approach to find the strongest connectivities between images. Finally, we demonstrate that our alignment algorithm improves the results of photometric stereo by showing normal maps before and after alignment.

@techreport{Berkiten:2014:AOI,
   author = "Sema Berkiten and Szymon Rusinkiewicz",
   title = "Alignment of Images Captured Under Different Light Directions",
   institution = "Princeton University",
   year = "2014",
   month = jul,
   number = " TR-974-14"
}