Belén Medrano

Integral Operators for Computing Homology Generators at Any Dimension

Starting from an nDgeometrical object, a cellular subdivision of such an object provides an algebraic counterpart from which homology information can be computed. In this paper, we develop a process to drastically reduce the amount of data that represent the original object, with the purpose of a subsequent homology computation. The technique applied is based on the construction of a sequence of elementary chain homotopies (integral operators) which algebraically connect the initial object with a simplified one with the same homological information than the former.

3D Well-composed Polyhedral Complexes

A binary three-dimensional (3D) image I is well-composed if the boundary surface of its continuous analog is a 2D manifold. Since 3D images are not often well-composed, there are several voxel-based methods (“repairing” algorithms) for turning them into well-composed ones but these methods either do not guarantee the topological equivalence between the original image and its corresponding well-composed one or involve sub-sampling the whole image. In this paper, we present a method to locally “repair” the cubical complex Q(I) (embedded in R 3 ) associated to I to obtain a polyhedral complex P(I) homotopy equivalent to Q(I) such that the boundary of every connected component of P(I) is a 2D manifold. The reparation is performed via a new codification system for P(I) under the form of a 3D grayscale image that allows an efficient access to cells and their faces.

Cubical Cohomology Ring of 3D Photographs

Cohomology groups and the cohomology ring of three‐dimensional (3D) objects are topological invariants that characterize holes and their relations. Cohomology ring has been traditionally computed on simplicial complexes. Nevertheless, cubical complexes deal directly with the voxels in 3D images, no additional triangulation is necessary. This could facilitate efficient algorithms for the computation of topological invariants in the image context. In this article, we present a constructive process, made up by several algorithms, to compute the cohomology ring of 3D binary‐valued digital photographs represented by cubical complexes. Starting from a cubical complex Q that represents such a 3D picture whose foreground has one connected component, we first compute the homological information on the boundary of the object, ∂Q, by an incremental technique; using a face reduction algorithm, we then compute it on the whole object; finally, applying explicit formulas for cubical complexes (without making use of any additional triangulation), the cohomology ring is computed from such information.

Well-composed cell complexes

Well-composed 3D digital images, which are 3D binary digital images whose boundary surface is made up by 2D manifolds, enjoy important topological and geometric properties that turn out to be advantageous for some applications. In this paper, we present a method to transform the cubical complex associated to a 3D binary digital image (which is not generally a well-composed image) into a cell complex that is homotopy equivalent to the first one and whose boundary surface is composed by 2D manifolds. This way, the new representation of the digital image can benefit from the application of algorithms that are developed over surfaces embedded in R3.

A tool for integer homology computation: λ-AT-model

In this paper, we formalize the notion of @l-AT-model (where @l is a non-null integer) for a given chain complex, which allows the computation of homological information in the integer domain avoiding using the Smith Normal Form of the boundary matrices. We present an algorithm for computing such a model, obtaining Betti numbers, the prime numbers p involved in the invariant factors of the torsion subgroup of homology, the amount of invariant factors that are a power of p and a set of representative cycles of generators of homology modp, for each p. Moreover, we establish the minimum valid @l for such a construction, what cuts down the computational costs related to the torsion subgroup. The tools described here are useful to determine topological information of nD structured objects such as simplicial, cubical or simploidal complexes and are applicable to extract such an information from digital pictures.

Reusing integer homology information of binary digital images

In this paper, algorithms for computing integer (co)homology of a simplicial complex of any dimension are designed, extending the work done in [1,2,3] For doing this, the homology of the object is encoded in an algebraic-topological format (that we call AM-model) Moreover, in the case of 3D binary digital images, having as input AM-models for the images I and J, we design fast algorithms for computing the integer homology of I ∪J, I ∩J and I ∖J.

Extending the notion of AT-model for integer homology computation

When the ground ring is a field, the notion of algebraic topological model (AT-model) is a useful tool for computing (co)homology, representative (co)cycles of (co)homology generators and the cup product on cohomology of nD digital images as well as for controlling topological information when the image suffers local changes [6,7,9]. In this paper, we formalize the notion of λ-AT-model (λ being an integer) which extends the one of AT-model and allows the computation of homological information in the integer domain without computing the Smith Normal Form of the boundary matrices. We present an algorithm for computing such a model, obtaining Betti numbers, the prime numbers p involved in the invariant factors (corresponding to the torsion subgroup of the homology), the amount of invariant factors that are a power of p and a set of representative cycles of the generators of homology mod p, for such p.

Efficiently Storing Well-Composed Polyhedral Complexes Computed Over 3D Binary Images

A 3D binary image I can be naturally represented by a combinatorial-algebraic structure called cubical complex and denoted by Q(I), whose basic building blocks are vertices, edges, square faces and cubes. In Gonzalez-Diaz et al. (Discret Appl Math 183:59–77, 2015), we presented a method to “locally repair” Q(I) to obtain a polyhedral complex P(I) (whose basic building blocks are vertices, edges, specific polygons and polyhedra), homotopy equivalent to Q(I), satisfying that its boundary surface is a 2D manifold. P(I) is called a well-composed polyhedral complex over the pictureI. Besides, we developed a new codification system for P(I), encoding geometric information of the cells of P(I) under the form of a 3D grayscale image, and the boundary face relations of the cells of P(I) under the form of a set of structuring elements. In this paper, we build upon (Gonzalez-Diaz et al. 2015) and prove that, to retrieve topological and geometric information of P(I), it is enough to store just one 3D point per polyhedron and hence neither grayscale image nor set of structuring elements are needed. From this “minimal” codification of P(I), we finally present a method to compute the 2-cells in the boundary surface of P(I).

Designing a Topological Algorithm for 3D Activity Recognition

Voxel carving is a non-invasive and low-cost technique that is used for the reconstruction of a 3D volume from images captured from a set of cameras placed around the object of interest. In this paper we propose a method to topologically analyze a video sequence of 3D reconstructions representing a tennis player performing different forehand and backhand strokes with the aim of providing an approach that could be useful in other sport activities.

Topological tracking of connected components in image sequences

Persistent homology provides information about the lifetime of homology classes along a filtration of cell complexes. Persistence barcode is a graphical representation of such information. A filtration might be determined by time in a set of spatiotemporal data, but classical methods for computing persistent homology do not respect the fact that we can not move backwards in time. In this paper, taking as input a time-varying sequence of two-dimensional (2D) binary digital images, we develop an algorithm for encoding, in the so-called {\it spatiotemporal barcode}, lifetime of connected components (of either the foreground or background) that are moving in the image sequence over time (this information may not coincide with the one provided by the persistence barcode). This way, given a connected component at a specific time in the sequence, we can track the component backwards in time until the moment it was born, by what we call a {\it spatiotemporal path}. The main contribution of this paper with respect to our previous works lies in a new algorithm that computes spatiotemporal paths directly, valid for both foreground and background and developed in a general context, setting the ground for a future extension for tracking higher dimensional topological features in