Canonical Plane Segmentation Without Annotating Pixel-Level Object Regions for Image Registration

Two-dimensional (2D) image registration is a natural choice for simultaneous object pose estimation and object recognition. However, it was not designed to perform object segmentation, which is critical for object-picking applications in warehouse automation scenarios. In this study, we propose a unified 2D image registration framework that simultaneously performs image registration and object segmentation by introducing a deep segmentation network module to the 2D image registration framework. Our method is designed to automatically generate annotations for training the segmentation network module through the process of 2D image registration, that is, no additional manual annotations are required. Specifically, given initial object regions from the 2D image registration results, our method trains the segmentation network module to refine a pseudo-pixel-level object region and remove background pixels based on the pixel-level similarity of an aligned image pair. The experimental results on several picking object datasets demonstrated that the segmentation accuracy of our method was superior to that of existing weakly supervised segmentation methods, and our method simultaneously achieved better performance for object recognition and pose estimation. Furthermore, our segmentation network module smoothly cooperated with many existing 2D image registration techniques.