In this paper, we define the two dimensional topological map, a model which represents both topological and geometrical information of a two dimensional labeled image. Since this model is minimal, complete and unique, we can use it to define efficient image processing algorithms. The topological map is the last level of a map hierarchy. Each level represents the region boundaries of the image and is defined from the previous one in the hierarchy, thus giving a simple constructive definition. This model is similar to two existing structures but the main innovation of our approach is the progressive definition based on the successive map levels. These different maps can easily be extended in order to define the topological map in any dimension. Furthermore we provide an optimal extraction algorithm which extracts the different maps of the hierarchy in a single image scan. This algorithm is based on local configurations called precodes. Due to our constructive definition, different configurations are factorized which simplifies the implementation.
Removal and contraction are basic operations for several methods conceived in order to handle irregular image pyramids, for multi-level image analysis for instance. Such methods are often based upon graph-like representations which do not maintain all topological information, even for 2-dimensional images. We study the definitions of removal and contraction operations in the generalized maps framework. These combinatorial structures enable us to unambiguously represent the topology of a well-known class of subdivisions of n-dimensional (discrete) spaces. The results of this study make a basis for a further work about irregular pyramids of n-dimensional images.
In this paper we define the 3d topological map and give an optimal algorithm which computes it from a segmented image. This data structure encodes totally all the information given by the segmentation. More, it allows to continue segmentation either algorithmically or interactively. We propose an original approach which uses several levels of maps. This allows us to propose a reasonable and implementable solution where other approaches don't allow suitable solutions. Moreover our solution has been implemented and the theoretical results translate very well in practical applications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.