318 research outputs found

    Invariance properties of the multidimensional matching distance in Persistent Topology and Homology

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    Persistent Topology studies topological features of shapes by analyzing the lower level sets of suitable functions, called filtering functions, and encoding the arising information in a parameterized version of the Betti numbers, i.e. the ranks of persistent homology groups. Initially introduced by considering real-valued filtering functions, Persistent Topology has been subsequently generalized to a multidimensional setting, i.e. to the case of Rn\R^n-valued filtering functions, leading to studying the ranks of multidimensional homology groups. In particular, a multidimensional matching distance has been defined, in order to compare these ranks. The definition of the multidimensional matching distance is based on foliating the domain of the ranks of multidimensional homology groups by a collection of half-planes, and hence it formally depends on a subset of RnĂ—Rn\R^n\times\R^n inducing a parameterization of these half-planes. It happens that it is possible to choose this subset in an infinite number of different ways. In this paper we show that the multidimensional matching distance is actually invariant with respect to such a choice.Comment: 14 pages, 2 figure

    Tomographic reconstruction of 2-convex polyominoes using dual Horn clauses

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    Further steps on the reconstruction of convex polyominoes from orthogonal projections

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    A closed formula for the number of convex permutominoes

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    In this paper we determine a closed formula for the number of convex permutominoes of size n. We reach this goal by providing a recursive generation of all convex permutominoes of size n+1 from the objects of size n, according to the ECO method, and then translating this construction into a system of functional equations satisfied by the generating function of convex permutominoes. As a consequence we easily obtain also the enumeration of some classes of convex polyominoes, including stack and directed convex permutominoes

    A new approximation Algorithm for the Matching Distance in Multidimensional Persistence

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    Topological Persistence has proven to be a promising framework for dealing with problems concerning shape analysis and comparison. In this contexts, it was originally introduced by taking into account 1-dimensional properties of shapes, modeled by real-valued functions. More recently, Topological Persistence has been generalized to consider multidimensional properties of shapes, coded by vector-valued functions. This extension has led to introduce suitable shape descriptors, named the multidimensional persistence Betti numbers functions, and a distance to compare them, the so-called multidimensional matching distance. In this paper we propose a new computational framework to deal with the multidimensional matching distance. We start by proving some new theoretical results, and then we use them to formulate an algorithm for computing such a distance up to an arbitrary threshold error

    Necessary Conditions for Discontinuities of Multidimensional Size Functions

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    Some new results about multidimensional Topological Persistence are presented, proving that the discontinuity points of a k-dimensional size function are necessarily related to the pseudocritical or special values of the associated measuring function.Comment: 23 pages, 4 figure

    Multidimensional persistent homology is stable

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    Multidimensional persistence studies topological features of shapes by analyzing the lower level sets of vector-valued functions. The rank invariant completely determines the multidimensional analogue of persistent homology groups. We prove that multidimensional rank invariants are stable with respect to function perturbations. More precisely, we construct a distance between rank invariants such that small changes of the function imply only small changes of the rank invariant. This result can be obtained by assuming the function to be just continuous. Multidimensional stability opens the way to a stable shape comparison methodology based on multidimensional persistence.Comment: 14 pages, 3 figure

    On the geometrical properties of the coherent matching distance in 2D persistent homology

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    In this paper we study a new metric for comparing Betti numbers functions in bidimensional persistent homology, based on coherent matchings, i.e. families of matchings that vary in a continuous way. We prove some new results about this metric, including its stability. In particular, we show that the computation of this distance is strongly related to suitable filtering functions associated with lines of slope 1, so underlining the key role of these lines in the study of bidimensional persistence. In order to prove these results, we introduce and study the concepts of extended Pareto grid for a normal filtering function as well as of transport of a matching. As a by-product, we obtain a theoretical framework for managing the phenomenon of monodromy in 2D persistent homology.Comment: 39 pages, 15 figures. Corrected the definition of multiplicity of points in the extended Pareto grid and the definition of normal function. Removed Rem. 3.3. Added Ex. 3.9, Fig. 11, Fig. 12, Rem. 5.3 and Fig. 15. Changed Rem. 4.9 into regular text. Reformulated statements of Theorems 5.1, 5.2, 5.4. Some changes in their proofs. Added references. Some small changes in the text and in the figure

    Keypics: free–hand drawn iconic keywords

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    We propose an iconic indexing of images to be exposed on the Web. This should be accomplished by “Keypics”, i.e. auxiliary, simplified pictures referring to the geometrical and/or the semantic content of the indexed image. Keypics should not be rigidly standardized; they should be left free to evolve, to express nuances and to stress details. A mathematical tool for dealing with such freedom, in the retrieval task, already exists: Size Functions. An experiment on 494 Keypics with Size Functions based on three measuring functions (distances, projections and jumps) and their combination is presented
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