76,844 research outputs found
On the complexity of optimal homotopies
In this article, we provide new structural results and algorithms for the
Homotopy Height problem. In broad terms, this problem quantifies how much a
curve on a surface needs to be stretched to sweep continuously between two
positions. More precisely, given two homotopic curves and
on a combinatorial (say, triangulated) surface, we investigate the problem of
computing a homotopy between and where the length of the
longest intermediate curve is minimized. Such optimal homotopies are relevant
for a wide range of purposes, from very theoretical questions in quantitative
homotopy theory to more practical applications such as similarity measures on
meshes and graph searching problems.
We prove that Homotopy Height is in the complexity class NP, and the
corresponding exponential algorithm is the best one known for this problem.
This result builds on a structural theorem on monotonicity of optimal
homotopies, which is proved in a companion paper. Then we show that this
problem encompasses the Homotopic Fr\'echet distance problem which we therefore
also establish to be in NP, answering a question which has previously been
considered in several different settings. We also provide an O(log
n)-approximation algorithm for Homotopy Height on surfaces by adapting an
earlier algorithm of Har-Peled, Nayyeri, Salvatipour and Sidiropoulos in the
planar setting
Algorithms to automatically quantify the geometric similarity of anatomical surfaces
We describe new approaches for distances between pairs of 2-dimensional
surfaces (embedded in 3-dimensional space) that use local structures and global
information contained in inter-structure geometric relationships. We present
algorithms to automatically determine these distances as well as geometric
correspondences. This is motivated by the aspiration of students of natural
science to understand the continuity of form that unites the diversity of life.
At present, scientists using physical traits to study evolutionary
relationships among living and extinct animals analyze data extracted from
carefully defined anatomical correspondence points (landmarks). Identifying and
recording these landmarks is time consuming and can be done accurately only by
trained morphologists. This renders these studies inaccessible to
non-morphologists, and causes phenomics to lag behind genomics in elucidating
evolutionary patterns. Unlike other algorithms presented for morphological
correspondences our approach does not require any preliminary marking of
special features or landmarks by the user. It also differs from other seminal
work in computational geometry in that our algorithms are polynomial in nature
and thus faster, making pairwise comparisons feasible for significantly larger
numbers of digitized surfaces. We illustrate our approach using three datasets
representing teeth and different bones of primates and humans, and show that it
leads to highly accurate results.Comment: Changes with respect to v1, v2: an Erratum was added, correcting the
references for one of the three datasets. Note that the datasets and code for
this paper can be obtained from the Data Conservancy (see Download column on
v1, v2
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