CEA Saclay and LASMEA, UMR, CNRS/UBP, France.
IEEE Trans Pattern Anal Mach Intell. 2010 Jan;32(1):87-104. doi: 10.1109/TPAMI.2008.265.
The registration problem for images of a deforming surface has been well studied. External occlusions are usually well handled. In 2D image-based registration, self-occlusions are more challenging. Consequently, the surface is usually assumed to be only slightly self-occluding. This paper is about image-based nonrigid registration with self-occlusion reasoning. A specific framework explicitly modeling self-occlusions is proposed. It is combined with an intensity-based, "direct" data term for registration. Self-occlusions are detected as shrinkage areas in the 2D warp. Experimental results on several challenging data sets show that our approach successfully registers images with self-occlusions while effectively detecting the self-occluded regions.
变形表面图像的配准问题已经得到了很好的研究。外部遮挡通常可以很好地处理。在基于 2D 图像的配准中，自遮挡更具挑战性。因此，通常假设表面只有轻微的自遮挡。本文是关于具有自遮挡推理的基于图像的非刚性配准。提出了一个专门用于显式建模自遮挡的特定框架。它与基于强度的“直接”数据项相结合，用于注册。自遮挡在 2D 变形中被检测为收缩区域。在几个具有挑战性的数据集上的实验结果表明，我们的方法能够成功地对具有自遮挡的图像进行配准，同时有效地检测出自遮挡区域。
