Search results for "Disjoint sets"

SURVEY Towards a global view of dynamical systems, for the C1-topology

2011

AbstractThis paper suggests a program for getting a global view of the dynamics of diffeomorphisms, from the point of view of the C1-topology. More precisely, given any compact manifold M, one splits Diff1(M) into disjoint C1-open regions whose union is C1-dense, and conjectures state that each of these open sets and their complements is characterized by the presence of: •either a robust local phenomenon;•or a global structure forbidding this local phenomenon. Other conjectures state that some of these regions are empty. This set of conjectures draws a global view of the dynamics, putting in evidence the coherence of the numerous recent results on C1-generic dynamics.

Multiple testing of pairs of one-sided hypotheses

1986

Two-sided test procedures fork real parameters should point out in the case of rejection whether the left or the right alternative can be assumed. This sets up a multiple testing problem fork pairs of one-sided hypotheses. Holm's (1979, Scandinavian Journal of Statistics 6:65–70) sequentially rejective test provides a solution the critical levels of which are slightly improved. Considerable improvement is obtained when the hypotheses are redefined to be disjoint in pairs.

Hard-Core Thinnings of Germ‒Grain Models with Power-Law Grain Sizes

2013

Random sets with long-range dependence can be generated using a Boolean model with power-law grain sizes. We study thinnings of such Boolean models which have the hard-core property that no grains overlap in the resulting germ‒grain model. A fundamental question is whether long-range dependence is preserved under such thinnings. To answer this question, we study four natural thinnings of a Poisson germ‒grain model where the grains are spheres with a regularly varying size distribution. We show that a thinning which favors large grains preserves the slow correlation decay of the original model, whereas a thinning which favors small grains does not. Our most interesting finding concerns the c…

Preventing Overlaps in Agglomerative Hierarchical Conceptual Clustering

2020

Hierarchical Clustering is an unsupervised learning task, whi-ch seeks to build a set of clusters ordered by the inclusion relation. It is usually assumed that the result is a tree-like structure with no overlapping clusters, i.e., where clusters are either disjoint or nested. In Hierarchical Conceptual Clustering (HCC), each cluster is provided with a conceptual description which belongs to a predefined set called the pattern language. Depending on the application domain, the elements in the pattern language can be of different nature: logical formulas, graphs, tests on the attributes, etc. In this paper, we tackle the issue of overlapping concepts in the agglomerative approach of HCC. We …

Subharmonic variation of the leafwise Poincar� metric

2003

Let X be a compact complex algebraic surface and let F be a holomorphic foliation, possibly with singularities, on X. On each leaf of F we put its Poincare metric (this will be defined below in more precise terms). We thus obtain a (singular) hermitian metric on the tangent bundle TF of F , and dually a (singular) hermitian metric on the canonical bundle KF = T ∗ F of F . The main aim of this paper is to prove that this metric on KF has positive curvature, in the sense of currents. Of course, the positivity of the curvature in the leaf direction is an immediate consequence of the definitions; the nontrivial fact is that the curvature is positive also in the directions transverse to the leaf…

2021

Extending CSG with projections: Towards formally certified geometric modeling

2015

We extend traditional Constructive Solid Geometry (CSG) trees to support the projection operator. Existing algorithms in the literature prove various topological properties of CSG sets. Our extension readily allows these algorithms to work on a greater variety of sets, in particular parametric sets, which are extensively used in CAD/CAM systems. Constructive Solid Geometry allows for algebraic representation which makes it easy for certification tools to apply. A geometric primitive may be defined in terms of a characteristic function, which can be seen as the zero-set of a corresponding system along with inequality constraints. To handle projections, we exploit the Disjunctive Normal Form,…

Improved rotation invariant pattern recognition using circular harmonics of binary gray level slices

2000

We introduce a new rotation invariant pattern recognition method based on nonlinear correlation. The images are decomposed into disjoint binary slices and then correlated using the common linear correlation. This operation is very discriminant even when the target is embedded in strong noise. We extend our sliced orthogonal nonlinear generalized correlation method to rotation invariant pattern recognition by combining the information of a circular harmonic (CH) of each binary slice of the reference object with binary slices of the target. In addition to improved discrimination capability, the method avoids the time-consuming process of finding proper centers for the CHs. Results are present…

Modified LACIF filtering in background disjoint noise

2011

Abstract This work deals with pattern recognition methods based on correlations for images in the presence of noise. We propose a modification of the nonlinear Locally Adaptive Contrast Invariant Filter (LACIF) that yields correlation peaks that are invariant to linear intensity changes of the target but that has some limitations in the presence low variance nonoverlapping background noise. The modification of the filter implies a normalization by a global variance of several distributions. The estimation of the variance distributions is done locally by means of correlations. Experimental results as well as comparisons with the classical matched filter and the common LACIF are given.

Intensity invariant nonlinear correlation filtering in spatially disjoint noise.

2010

We analyze the performance of a nonlinear correlation called the Locally Adaptive Contrast Invariant Filter in the presence of spatially disjoint noise under the peak-to-sidelobe ratio (PSR) metric. We show that the PSR using the nonlinear correlation improves as the disjoint noise intensity increases, whereas, for common linear filtering, it goes to zero. Experimental results as well as comparisons with a classical matched filter are given.