Inf-structuring Functions: A Unifying Theory of Connections and Connected Operators

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• 作者：Benjamin Perret (1)
  
  1. Laboratoire d鈥橧nformatique Gaspard-Monge ; Universit茅 Paris-Est ; 脡quipe A3SI ; ESIEE Paris ; 2 ; Boulevard Blaise Pascal ; Cit茅 Descartes ; BP 99 ; 93162聽 ; Noisy le Grand Cedex ; France
  
• 关键词：Inf ; structuring function ; Connection ; Hyperconnection ; Attribute space connection ; Connected operator ; Mathematical morphology
• 刊名：Journal of Mathematical Imaging and Vision
• 出版年：2015
• 出版时间：January 2015
• 年：2015
• 卷：51
• 期：1
• 页码：171-194
• 全文大小：946 KB
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• 刊物类别：Computer Science
• 刊物主题：Computer Imaging, Vision, Pattern Recognition and Graphics
  Image Processing and Computer Vision
  Artificial Intelligence and Robotics
  Automation and Robotics
  
• 出版者：Springer Netherlands
• ISSN：1573-7683

文摘

During the last decade, several theories have been proposed in order to extend the notion of set connections in mathematical morphology. These new theories were obtained by generalizing the definition to wider spaces (namely complete lattices) and/or by relaxing some hypothesis. Nevertheless, the links among those different theories are not always well understood, and this work aims at defining a unifying theoretical framework. The adopted approach relies on the notion of inf-structuring function which is simply a mapping that associates a set of sub-elements to each element of the space. The developed theory focuses on the properties of the decompositions given by an inf-structuring function rather than in trying to characterize the properties of the set of connected elements as a whole. We establish several sets of inf-structuring function properties that enable to recover the existing notions of connections, hyperconnections, and attribute space connections. Moreover, we also study the case of grey-scale connected operators that are obtained by stacking set connected operators and we show that they can be obtained using specific inf-structuring functions. This work allows us to better understand the existing theories, it facilitates the reuse of existing results among the different theories and it gives a better view on the unexplored areas of the connection theories.