IEEE Trans Image Process. 2014 Dec;23(12):5152-64. doi: 10.1109/TIP.2014.2362053. Epub 2014 Oct 8.
In recent papers, a new notion of component-graph was introduced. It extends the classical notion of component-tree initially proposed in mathematical morphology to model the structure of gray-level images. Component-graphs can indeed model the structure of any-gray-level or multivalued-images. We now extend the antiextensive filtering scheme based on component-trees, to make it tractable in the framework of component-graphs. More precisely, we provide solutions for building a component-graph, reducing it based on selection criteria, and reconstructing a filtered image from a reduced component-graph. In this paper, we first consider the cases where component-graphs still have a tree structure; they are then called multivalued component-trees. The relevance and usefulness of such multivalued component-trees are illustrated by applicative examples on hierarchically classified remote sensing images.
在最近的一些论文中，引入了一种新的组件图概念。它扩展了最初在数学形态学中提出的经典组件树概念，以对灰度图像的结构进行建模。组件图确实可以对任何灰度或多值图像的结构进行建模。现在，我们扩展了基于组件树的反扩展滤波方案，以便在组件图框架中使其更易于处理。更准确地说，我们提供了用于构建组件图、根据选择标准对其进行简化以及从简化的组件图重建滤波图像的解决方案。在本文中，我们首先考虑组件图仍然具有树结构的情况；然后它们被称为多值组件树。通过对分层分类遥感图像的应用示例，说明了这种多值组件树的相关性和有用性。
