Fast Graph-Based Object Segmentation for RGB-D Images
This work addresses object segmentation for robotic systems, but it is incremental as it builds on existing graph-based techniques without introducing major innovations.
The authors tackled object segmentation for robotic grasping by developing a fast graph-based method that uses modified Canny edge detection and cost functions to combine color and depth cues, achieving O(NlogN) complexity and testing on public RGB-D datasets.
Object segmentation is an important capability for robotic systems, in particular for grasping. We present a graph- based approach for the segmentation of simple objects from RGB-D images. We are interested in segmenting objects with large variety in appearance, from lack of texture to strong textures, for the task of robotic grasping. The algorithm does not rely on image features or machine learning. We propose a modified Canny edge detector for extracting robust edges by using depth information and two simple cost functions for combining color and depth cues. The cost functions are used to build an undirected graph, which is partitioned using the concept of internal and external differences between graph regions. The partitioning is fast with O(NlogN) complexity. We also discuss ways to deal with missing depth information. We test the approach on different publicly available RGB-D object datasets, such as the Rutgers APC RGB-D dataset and the RGB-D Object Dataset, and compare the results with other existing methods.