Loops in surfaces associated with topological features such as handles and tunnels are important entities in many applications including surface parameterization, feature identification, and topological simplification. Recently, a persistent homology based algorithm has been proposed to compute them. The algorithm has several advantages including its simplicity, combinatorial nature and indepen...

We present an efficient algorithm for computing a function that minimizes the number of critical points among all functions within a prescribed distance δ from a given input function. The result is achieved by establishing a connection between discrete Morse theory and persistent homology. Our method completely removes homological noise with persistence less than 2δ, constructively proving that...

In the setting of persistent homology computation, a useful tool is the persistence barcode representation in which pairs of birth and death times of homology classes are encoded in the form of intervals. Starting from a polyhedral complex K (an object subdivided into cells which are polytopes) and an initial order of the set of vertices, we are concerned with the general problem of searching f...

While there has been much interest in adapting conventional clustering procedures—and in higher dimensions, persistent homology methods—to directed networks, little is known about the convergence of such methods. In order to even formulate the problem of convergence for such methods, one needs to stipulate a reasonable model for a directed network together with a flexible sampling theory for su...

This paper presents a new hybrid split-and-merge image segmentation method based on computational geometry and topology using persistent homology. The algorithm uses edge-directed topology to initially split the image into a set of regions based on the Delaunay triangulations of the points in the edge map. Persistent homology is used to generate three types of regions: p-persistent regions, p-t...

In this paper we examine the use of topological methods for multivariate statistics. Using persistent homology from computational algebraic topology, a random sample is used to construct estimators of persistent homology. This estimation procedure can then be evaluated using the bottleneck distance between the estimated persistent homology and the true persistent homology. The connection to sta...

We describe an approach to bounded-memory computation of persistent homology and betti barcodes, in which a computational state is maintained with updates introducing new edges to the underlying neighbourhood graph and percolating the resulting changes into the simplex stream feeding the persistence algorithm. We further discuss the memory consumption and resulting speed and complexity behaviou...

We consider the problem of statistical computations with persistence diagrams, a summary representation of topological features in data. These diagrams encode persistent homology, a widely used invariant in topological data analysis. While several avenues towards a statistical treatment of the diagrams have been explored recently, we follow an alternative route that is motivated by the success ...

Persistence theory discussed in this paper 1 is an application of algebraic topology (Morse Theory) to Data Analysis, precisely to qualitative understanding of point cloud data. Mathematically a point cloud data is a finite metric space of a very large cardinality. It can be geometrized as a filtration of simplicial complexes and the homology changes of these complexes provide qualitative infor...

Persistence homology is a tool used to measure topological features that are present in data sets and functions. Persistence pairs births and deaths of these features as we iterate through the sublevel sets of the data or function of interest. I am concerned with using persistence to characterize the difference between two functions f, g : M ->R, where M is a topological space. Furthermore, I f...