‘Object-Based Image Analysis Using Multiscale Connectivity.’ This paper precedes a way for image analysis based on the concept of multiscale connectivity. The authors have suggested an approach to design several tools for object-based image representation and analysis, which attain the connectivity structure of images in a multiscale fashion. More specifically, they have suggested a nonlinear pyramidal image representation scheme, which decomposes an image at various scales by means of multiscale grain filters. These filters progressively remove connected components from an image that fail to satisfy a given benchmark. They have also used the concept of multiscale connectivity to design a hierarchical data partitioning tool and apply this to construct another image representation scheme, based on the theory of component trees, which organizes partitions of an image in a hierarchical multiscale fashion. They have also suggested a geometrically-oriented hierarchical clustering algorithm which generalizes the classical single-linkage algorithm. Finally suggested two object-based multiscale image summaries, similar to the well-known pattern spectrum, which can be useful in image analysis and image understanding applications.
Multiscale connectivity was introduced by extending a general theory of connectivity on complete lattices, to a multiscale setting. The idea of multiscale connectivity emerges from the observation that the connectivity of an object may depend on the particular scale at which it is observed. The dependence of connectivity on scale can be equivalently represented by a connectivity measure, which specifies the degree of connectivity of an object, or by a connectivity pyramid, which is a nested sequence of connectivity classes that depend on scale. In this paper, it is shown that the idea of multiscale connectivity leads to a number of tools for object based image analysis. Several methods are introduced, which include object-based...
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