Hierarchical and View-invariant Light Field Segmentation by Maximizing Entropy Rate on 4D Ray Graphs
Abstract
Image segmentation is an important first step of many image processing, computer graphics, and computer vision pipelines. Unfortunately, it remains difficult to automatically and robustly segment cluttered scenes, or scenes in which multiple objects have similar color and texture. In these scenarios, light fields offer much richer cues that can be used efficiently to drastically improve the quality and robustness of segmentations. In this paper we introduce a new light field segmentation method that respects texture appearance, depth consistency, as well as occlusion, and creates well-shaped segments that are robust under viewpoint changes. Furthermore, our segmentation is hierarchical, i.e. with a single optimization, a whole hierarchy of segmentations with different numbers of regions is available. All this is achieved with a submodular objective function that
