Image Science, Computer Science and Remote Sensing Laboratory (LSIIT, Unités Mixtes de Recherche 7005) University of Strasbourg–National Center for Scientific Research, Pôle API, Strasbourg, France.
IEEE Trans Image Process. 2012 Jan;21(1):14-27. doi: 10.1109/TIP.2011.2161322. Epub 2011 Jul 7.
Connections in image processing are an important notion that describes how pixels can be grouped together according to their spatial relationships and/or their gray-level values. In recent years, several works were devoted to the development of new theories of connections among which hyperconnection (h-connection) is a very promising notion. This paper addresses two major issues of this theory. First, we propose a new axiomatic that ensures that every h-connection generates decompositions that are consistent for image processing and, more precisely, for the design of h-connected filters. Second, we develop a general framework to represent the decomposition of an image into h-connections as a tree that corresponds to the generalization of the connected component tree. Such trees are indeed an efficient and intuitive way to design attribute filters or to perform detection tasks based on qualitative or quantitative attributes. These theoretical developments are applied to a particular fuzzy h-connection, and we test this new framework on several classical applications in image processing, i.e., segmentation, connected filtering, and document image binarization. The experiments confirm the suitability of the proposed approach: It is robust to noise, and it provides an efficient framework to design selective filters.
图像处理中的连通性是一个重要的概念，它描述了像素如何根据其空间关系和/或灰度值进行分组。近年来，人们致力于开发新的连接理论，其中超连接（h-连接）是一个非常有前途的概念。本文解决了该理论的两个主要问题。首先，我们提出了一个新的公理，该公理确保每个 h-连接生成的分解对于图像处理是一致的，更确切地说，对于 h 连通滤波器的设计是一致的。其次，我们开发了一个通用框架，将图像分解为 h-连接表示为一棵树，这对应于连通分量树的推广。这样的树确实是设计属性滤波器或基于定性或定量属性执行检测任务的有效和直观的方法。这些理论发展应用于特定的模糊 h-连接，我们在图像处理的几个经典应用中测试了这个新框架，即分割、连通滤波和文档图像二值化。实验证实了所提出方法的适用性：它对噪声具有鲁棒性，并且提供了一个有效的框架来设计选择性滤波器。
