Hyperconnections and Hierarchical Representations for Grayscale and Multiband Image Processing
Hyperconnections and Hierarchical Representations for Grayscale and Multiband Image Processing
复制标题
DOI：
10.1109/tip.2011.2161322
复制
发表时间：
2012
影响因子：
10.6
通讯作者：
B. Perret;S. Lefèvre;C. Collet;É. Slezak
中科院分区：
文献类型：
--
作者：
B. Perret;S. Lefèvre;C. Collet;É. Slezak
图像处理中的连接是一个重要的概念，它描述了如何根据像素的空间关系和/或灰度值将像素分组在一起。近年来，一些工作致力于发展新的连接理论，其中超连接（h-连接）是一个非常有前途的概念。本文讨论了这一理论的两个主要问题。首先，我们提出了一个新的公理，确保每个h-连接生成的分解是一致的图像处理，更准确地说，为h-连接过滤器的设计。其次，我们开发了一个通用的框架来表示分解的图像到h-连接作为一个树，对应于连接组件树的泛化。这种树确实是设计属性过滤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式。这些理论的发展被应用到一个特定的模糊h-连接，我们测试这个新的框架在图像处理中的几个经典应用，即，分割、连通滤波和文档图像二值化。实验结果证实了该方法的适用性：它是鲁棒的噪声，它提供了一个有效的框架来设计选择性滤波器。
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.