Multiple structure recovery with T-linkage

reserved2noThis work addresses the problem of robust fitting of geometric structures to noisy data corrupted by outliers. An extension of J-linkage (called T-linkage) is presented and elaborated. T-linkage improves the preference analysis implemented by J-linkage in term of performances and robustness, considering both the representation and the segmentation steps. A strategy to reject outliers and to estimate the inlier threshold is proposed, resulting in a versatile tool, suitable for multi-model fitting “in the wild”. Experiments demonstrate that our methods perform better than J-linkage on simulated data, and compare favorably with state-of-the-art methods on public domain real datasets.mixedMagri L.; Fusiello A.Magri, L.; Fusiello, A

Broad-band X-ray analysis of local mid-infrared selected Compton-thick AGN candidates

The estimate of the number and space density of obscured AGN over cosmic time still represents an open issue. While the obscured AGN population is a key ingredient of the X-ray background synthesis models and is needed to reproduce its shape, a complete census of obscured AGN is still missing. Here we test the selection of obscured sources among the local 12-micron sample of Seyfert galaxies. Our selection is based on a difference up to three orders of magnitude in the ratio between the AGN bolometric luminosity, derived from the spectral energy distribution (SED) decomposition, and the same quantity obtained by the published XMM-Newton 2-10 keV luminosity. The selected sources are UGC05101, NGC1194 and NGC3079 for which the available X-ray wide bandpass, from Chandra and XMM-Newton plus NuSTAR data, extending to energies up to ~30-45 keV, allows us an accurate determination of the column density, and hence of the true intrinsic power. The newly derived NH values clearly indicate heavy obscuration (about 1.2, 2.1 and 2.4 x10^{24} cm-2 for UGC05101, NGC1194 and NGC3079, respectively) and are consistent with the prominent silicate absorption feature observed in the Spitzer-IRS spectra of these sources (at 9.7 micron rest frame). We finally checked that the resulting X-ray luminosities in the 2-10 keV band are in good agreement with those derived from the mid-IR band through empirical L_MIR-L_X relations.Comment: 14 pages, 6 figures, accepted for publication in MNRA

Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting

In this paper, we propose a novel geometric model fitting method, called Mode-Seeking on Hypergraphs (MSH),to deal with multi-structure data even in the presence of severe outliers. The proposed method formulates geometric model fitting as a mode seeking problem on a hypergraph in which vertices represent model hypotheses and hyperedges denote data points. MSH intuitively detects model instances by a simple and effective mode seeking algorithm. In addition to the mode seeking algorithm, MSH includes a similarity measure between vertices on the hypergraph and a weight-aware sampling technique. The proposed method not only alleviates sensitivity to the data distribution, but also is scalable to large scale problems. Experimental results further demonstrate that the proposed method has significant superiority over the state-of-the-art fitting methods on both synthetic data and real images.Comment: Proceedings of the IEEE International Conference on Computer Vision, pp. 2902-2910, 201

Multiple structure recovery via robust preference analysis

2noThis paper address the extraction of multiple models from outlier-contaminated data by exploiting preference analysis and low rank approximation. First points are represented in the preference space, then Robust PCA (Principal Component Analysis) and Symmetric NMF (Non negative Matrix Factorization) are used to break the multi-model fitting problem into many single-model problems, which in turn are tackled with an approach inspired to MSAC (M-estimator SAmple Consensus) coupled with a model-specific scale estimate. Experimental validation on public, real data-sets demonstrates that our method compares favorably with the state of the art.openopenMagri, Luca; Fusiello, AndreaMagri, Luca; Fusiello, Andre

Inhomogeneity effects in Cosmology

This article looks at how inhomogeneous spacetime models may be significant for cosmology. First it looks at how the averaging process may affect large scale dynamics, with backreaction effects leading to effective contributions to the averaged energy-momentum tensor. Secondly it considers how local inhomogeneities may affect cosmological observations in cosmology, possibly significantly affecting the concordance model parameters. Thirdly it presents the possibility that the universe is spatially inhomogeneous on Hubble scales, with a violation of the Copernican principle leading to an apparent acceleration of the universe. This could perhaps even remove the need for the postulate of dark energy.Comment: 29 pages. For special issue of CQG on inhomogeneous cosmologie

The interpretation of crustal dynamics data in terms of plate motions and regional deformation near plate boundaries

The focus was in two broad areas during the most recent 6-month period: (1) the nature and dynamics of time dependent deformation and stress along major seismic zones; and (2) the nature of long-wavelength oceanic geoid anomalies in terms of lateral variations in upper mantle temperature and composition. The principle findings are described in the accompanying appendices

MULTIPLE STRUCTURE RECOVERY VIA PREFERENCE ANALYSIS IN CONCEPTUAL SPACE

Finding multiple models (or structures) that fit data corrupted by noise and outliers is an omnipresent problem in empirical sciences, includingComputer Vision, where organizing unstructured visual data in higher level geometric structures is a necessary and basic step to derive better descriptions and understanding of a scene. This challenging problem has a chicken-and-egg pattern: in order to estimate models one needs to first segment the data, and in order to segment the data it is necessary to know which structure points belong to. Most of the multi-model fitting techniques proposed in the literature can be divided in two classes, according to which horn of the chicken-egg-dilemma is addressed first, namely consensus and preference analysis. Consensus-based methods put the emphasis on the estimation part of the problem and focus on models that describe has many points as possible. On the other side, preference analysis concentrates on the segmentation side in order to find a proper partition of the data, from which model estimation follows. The research conducted in this thesis attempts to provide theoretical footing to the preference approach and to elaborate it in term of performances and robustness. In particular, we derive a conceptual space in which preference analysis is robustly performed thanks to three different formulations of multiple structures recovery, i.e. linkage clustering, spectral analysis and set coverage. In this way we are able to propose new and effective strategies to link together consensus and preferences based criteria to overcome the limitation of both. In order to validate our researches, we have applied our methodologies to some significant Computer Vision tasks including: geometric primitive fitting (e.g. line fitting; circle fitting; 3D plane fitting), multi-body segmentation, plane segmentation, and video motion segmentation