@inproceedings{3367,
abstract = {In this paper, we present the first output-sensitive algorithm to compute the persistence diagram of a filtered simplicial complex. For any Γ>0, it returns only those homology classes with persistence at least Γ. Instead of the classical reduction via column operations, our algorithm performs rank computations on submatrices of the boundary matrix. For an arbitrary constant δ ∈ (0,1), the running time is O(C(1-δ)ΓR(n)log n), where C(1-δ)Γ is the number of homology classes with persistence at least (1-δ)Γ, n is the total number of simplices, and R(n) is the complexity of computing the rank of an n x n matrix with O(n) nonzero entries. Depending on the choice of the rank algorithm, this yields a deterministic O(C(1-δ)Γn2.376) algorithm, a O(C(1-δ)Γn2.28) Las-Vegas algorithm, or a O(C(1-δ)Γn2+ε) Monte-Carlo algorithm for an arbitrary ε>0.},
author = {Chen, Chao and Kerber, Michael},
location = {Paris, France},
pages = {207 -- 216},
publisher = {ACM},
title = {{An output sensitive algorithm for persistent homology}},
doi = {10.1145/1998196.1998228},
year = {2011},
}
@inproceedings{3336,
abstract = {We introduce TopoCut: a new way to integrate knowledge about topological properties (TPs) into random field image segmentation model. Instead of including TPs as additional constraints during minimization of the energy function, we devise an efficient algorithm for modifying the unary potentials such that the resulting segmentation is guaranteed with the desired properties. Our method is more flexible in the sense that it handles more topology constraints than previous methods, which were only able to enforce pairwise or global connectivity. In particular, our method is very fast, making it for the first time possible to enforce global topological properties in practical image segmentation tasks.},
author = {Chen, Chao and Freedman, Daniel and Lampert, Christoph},
booktitle = {CVPR: Computer Vision and Pattern Recognition},
location = {Colorado Springs, CO, USA},
pages = {2089 -- 2096},
publisher = {IEEE},
title = {{Enforcing topological constraints in random field image segmentation}},
doi = {10.1109/CVPR.2011.5995503},
year = {2011},
}
@article{3267,
abstract = {We address the problem of localizing homology classes, namely, finding the cycle representing a given class with the most concise geometric measure. We study the problem with different measures: volume, diameter and radius. For volume, that is, the 1-norm of a cycle, two main results are presented. First, we prove that the problem is NP-hard to approximate within any constant factor. Second, we prove that for homology of dimension two or higher, the problem is NP-hard to approximate even when the Betti number is O(1). The latter result leads to the inapproximability of the problem of computing the nonbounding cycle with the smallest volume and computing cycles representing a homology basis with the minimal total volume. As for the other two measures defined by pairwise geodesic distance, diameter and radius, we show that the localization problem is NP-hard for diameter but is polynomial for radius. Our work is restricted to homology over the ℤ2 field.},
author = {Chen, Chao and Freedman, Daniel},
journal = {Discrete & Computational Geometry},
number = {3},
pages = {425 -- 448},
publisher = {Springer},
title = {{Hardness results for homology localization}},
doi = {10.1007/s00454-010-9322-8},
volume = {45},
year = {2011},
}
@inproceedings{3313,
abstract = {Interpreting an image as a function on a compact sub- set of the Euclidean plane, we get its scale-space by diffu- sion, spreading the image over the entire plane. This gener- ates a 1-parameter family of functions alternatively defined as convolutions with a progressively wider Gaussian ker- nel. We prove that the corresponding 1-parameter family of persistence diagrams have norms that go rapidly to zero as time goes to infinity. This result rationalizes experimental observations about scale-space. We hope this will lead to targeted improvements of related computer vision methods.},
author = {Chen, Chao and Edelsbrunner, Herbert},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
location = {Barcelona, Spain},
publisher = {IEEE},
title = {{Diffusion runs low on persistence fast}},
doi = {10.1109/ICCV.2011.6126271},
year = {2011},
}
@article{3332,
abstract = {Given an algebraic hypersurface O in ℝd, how many simplices are necessary for a simplicial complex isotopic to O? We address this problem and the variant where all vertices of the complex must lie on O. We give asymptotically tight worst-case bounds for algebraic plane curves. Our results gradually improve known bounds in higher dimensions; however, the question for tight bounds remains unsolved for d ≥ 3.},
author = {Kerber, Michael and Sagraloff, Michael},
journal = {Graphs and Combinatorics},
number = {3},
pages = {419 -- 430},
publisher = {Springer},
title = {{A note on the complexity of real algebraic hypersurfaces}},
doi = {10.1007/s00373-011-1020-7},
volume = {27},
year = {2011},
}
@article{3781,
abstract = {We bound the difference in length of two curves in terms of their total curvatures and the Fréchet distance. The bound is independent of the dimension of the ambient Euclidean space, it improves upon a bound by Cohen-Steiner and Edelsbrunner, and it generalizes a result by Fáry and Chakerian.},
author = {Fasy, Brittany Terese},
journal = {Acta Sci. Math. (Szeged)},
number = {1-2},
pages = {359 -- 367},
publisher = {Szegedi Tudományegyetem},
title = {{The difference in length of curves in R^n}},
volume = {77},
year = {2011},
}
@inproceedings{3782,
abstract = {In cortex surface segmentation, the extracted surface is required to have a particular topology, namely, a two-sphere. We present a new method for removing topology noise of a curve or surface within the level set framework, and thus produce a cortical surface with correct topology. We define a new energy term which quantifies topology noise. We then show how to minimize this term by computing its functional derivative with respect to the level set function. This method differs from existing methods in that it is inherently continuous and not digital; and in the way that our energy directly relates to the topology of the underlying curve or surface, versus existing knot-based measures which are related in a more indirect fashion. The proposed flow is validated empirically.},
author = {Chen, Chao and Freedman, Daniel},
booktitle = { Conference proceedings MCV 2010},
location = {Beijing, China},
pages = {31 -- 42},
publisher = {Springer},
title = {{Topology noise removal for curve and surface evolution}},
doi = {10.1007/978-3-642-18421-5_4},
volume = {6533},
year = {2010},
}
@inbook{3795,
abstract = {The (apparent) contour of a smooth mapping from a 2-manifold to the plane, f: M → R2 , is the set of critical values, that is, the image of the points at which the gradients of the two component functions are linearly dependent. Assuming M is compact and orientable and measuring difference with the erosion distance, we prove that the contour is stable.},
author = {Edelsbrunner, Herbert and Morozov, Dmitriy and Patel, Amit},
booktitle = {Topological Data Analysis and Visualization: Theory, Algorithms and Applications},
pages = {27 -- 42},
publisher = {Springer},
title = {{The stability of the apparent contour of an orientable 2-manifold}},
doi = {10.1007/978-3-642-15014-2_3},
year = {2010},
}
@inproceedings{3853,
abstract = {Quantitative languages are an extension of boolean languages that assign to each word a real number. Mean-payoff automata are finite automata with numerical weights on transitions that assign to each infinite path the long-run average of the transition weights. When the mode of branching of the automaton is deterministic, nondeterministic, or alternating, the corresponding class of quantitative languages is not robust as it is not closed under the pointwise operations of max, min, sum, and numerical complement. Nondeterministic and alternating mean-payoff automata are not decidable either, as the quantitative generalization of the problems of universality and language inclusion is undecidable. We introduce a new class of quantitative languages, defined by mean-payoff automaton expressions, which is robust and decidable: it is closed under the four pointwise operations, and we show that all decision problems are decidable for this class. Mean-payoff automaton expressions subsume deterministic meanpayoff automata, and we show that they have expressive power incomparable to nondeterministic and alternating mean-payoff automata. We also present for the first time an algorithm to compute distance between two quantitative languages, and in our case the quantitative languages are given as mean-payoff automaton expressions.},
author = {Chatterjee, Krishnendu and Doyen, Laurent and Edelsbrunner, Herbert and Henzinger, Thomas A and Rannou, Philippe},
location = {Paris, France},
pages = {269 -- 283},
publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik},
title = {{Mean-payoff automaton expressions}},
doi = {10.1007/978-3-642-15375-4_19},
volume = {6269},
year = {2010},
}
@inproceedings{3848,
abstract = {We define the robustness of a level set homology class of a function f:XR as the magnitude of a perturbation necessary to kill the class. Casting this notion into a group theoretic framework, we compute the robustness for each class, using a connection to extended persistent homology. The special case X=R3 has ramifications in medical imaging and scientific visualization.},
author = {Bendich, Paul and Edelsbrunner, Herbert and Morozov, Dmitriy and Patel, Amit},
location = {Liverpool, UK},
pages = {1 -- 10},
publisher = {Springer},
title = {{The robustness of level sets}},
doi = {10.1007/978-3-642-15775-2_1},
volume = {6346},
year = {2010},
}