Describing shapes by geometrical-topological properties of real functions

Abstract

Differential topology, and specifically Morse theory, provide a suitable setting for formalizing and solving several problems related to shape analysis. The fundamental idea behind Morse theory is that of combining the topological exploration of a shape with quantitative measurement of geometrical properties provided by a real function defined on the shape. The added value of approaches based on Morse theory is in the possibility of adopting different functions as shape descriptors according to the properties and invariants that one wishes to analyze. In this sense, Morse theory allows one to construct a general framework for shape characterization, parametrized with respect to the mapping function used, and possibly the space associated with the shape. The mapping function plays the role of a lens through which we look at the properties of the shape, and different functions provide different insights. In the last decade, an increasing number of methods that are rooted in Morse theory and make use of properties of real-valued functions for describing shapes have been proposed in the literature. The methods proposed range from approaches which use the configuration of contours for encoding topographic surfaces to more recent work on size theory and persistent homology. All these have been developed over the years with a specific target domain and it is not trivial to systematize this work and understand the links, similarities, and differences among the different methods. Moreover, different terms have been used to denote the same mathematical constructs, which often overwhelm the understanding of the underlying common framework. The aim of this survey is to provide a clear vision of what has been developed so far, focusing on methods that make use of theoretical frameworks that are developed for classes of real functions rather than for a single function, even if they are applied in a restricted manner. The term geometrical-topological used in the title is meant to underline that both levels of information content are relevant for the applications of shape descriptions: geometrical, or metrical, properties and attributes are crucial for characterizing specific instances of features, while topological properties are necessary to abstract and classify shapes according to invariant aspects of their geometry. The approaches surveyed will be discussed in detail, with respect to theory, computation, and application. Several properties of the shape descriptors will be analyzed and compared. We believe this is a crucial step to exploit fully the potential of such approaches in many applications, as well as to identify important areas of future research.

A new algorithm for computing the 2-dimensional matching distance between size functions

“…The cost of computing a 1-dimensional size function on a mesh with m vertices takes O m log m . Computing the 1-dimensional matching distance between two 1-dimensional size functions takes O p 2.5 , being p the total number of cornerpoints of the two descriptors [3]. Hence, the overall computational complexity depends on the complexities above, multiplied by the number of points in P.…”

“…Size functions are complete and stable descriptors, admitting a simple and compact representation made up of a multiset of points in the Euclidean plane, and are compared using a suitable matching distance [3].…”

Hierarchical Shape Segmentation and Registration via Topological Features of Laplace-Beltrami Eigenfunctions

“…The reader is referred to [38,39] for more background on Morse theory and on the Morse-Smale complex. A good overview also treating the evolution of the used concepts (size functions, Morse-Smale complex, Reeb graph and persistent homology) can be found in [40].…”

Multi-scale Approximation of the Matching Distance for Shape Retrieval

“…Vice-versa, noise and shape details are characterized by a shorter life. For further details we refer to [1,9].…”

“…Topological Persistenceincluding Persistent Homology [9] and Size Theory [1,10] -has proven to be a successful comparison/retrieval/classification (hereafter CRC) scheme.…”