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

Robust Motion Segmentation from Pairwise Matches

In this paper we address a classification problem that has not been considered before, namely motion segmentation given pairwise matches only. Our contribution to this unexplored task is a novel formulation of motion segmentation as a two-step process. First, motion segmentation is performed on image pairs independently. Secondly, we combine independent pairwise segmentation results in a robust way into the final globally consistent segmentation. Our approach is inspired by the success of averaging methods. We demonstrate in simulated as well as in real experiments that our method is very effective in reducing the errors in the pairwise motion segmentation and can cope with large number of mismatches

CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus

We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements. Applications include finding multiple vanishing points in man-made scenes, fitting planes to architectural imagery, or estimating multiple rigid motions within the same sequence. In contrast to previous works, which resorted to hand-crafted search strategies for multiple model detection, we learn the search strategy from data. A neural network conditioned on previously detected models guides a RANSAC estimator to different subsets of all measurements, thereby finding model instances one after another. We train our method supervised as well as self-supervised. For supervised training of the search strategy, we contribute a new dataset for vanishing point estimation. Leveraging this dataset, the proposed algorithm is superior with respect to other robust estimators as well as to designated vanishing point estimation algorithms. For self-supervised learning of the search, we evaluate the proposed algorithm on multi-homography estimation and demonstrate an accuracy that is superior to state-of-the-art methods.Comment: CVPR 202

Multi-model fitting based on minimum spanning tree

This paper presents a novel approach to the computation of primitive geometrical structures, where no prior knowledge about the visual scene is available and a high level of noise is expected. We based our work on the grouping principles of proximity and similarity, of points and preliminary models. The former was realized using Minimum Spanning Trees (MST), on which we apply a stable alignment and goodness of fit criteria. As for the latter, we used spectral clustering of preliminary models. The algorithm can be generalized to various model fitting settings, without tuning of run parameters. Experiments demonstrate the significant improvement in the localization accuracy of models in plane, homography and motion segmentation examples. The efficiency of the algorithm is not dependent on fine tuning of run parameters like most others in the field

Holistic interpretation of visual data based on topology:semantic segmentation of architectural facades

The work presented in this dissertation is a step towards effectively incorporating contextual knowledge in the task of semantic segmentation. To date, the use of context has been confined to the genre of the scene with a few exceptions in the field. Research has been directed towards enhancing appearance descriptors. While this is unarguably important, recent studies show that computer vision has reached a near-human level of performance in relying on these descriptors when objects have stable distinctive surface properties and in proper imaging conditions. When these conditions are not met, humans exploit their knowledge about the intrinsic geometric layout of the scene to make local decisions. Computer vision lags behind when it comes to this asset. For this reason, we aim to bridge the gap by presenting algorithms for semantic segmentation of building facades making use of scene topological aspects. We provide a classification scheme to carry out segmentation and recognition simultaneously.The algorithm is able to solve a single optimization function and yield a semantic interpretation of facades, relying on the modeling power of probabilistic graphs and efficient discrete combinatorial optimization tools. We tackle the same problem of semantic facade segmentation with the neural network approach.We attain accuracy figures that are on-par with the state-of-the-art in a fully automated pipeline.Starting from pixelwise classifications obtained via Convolutional Neural Networks (CNN). These are then structurally validated through a cascade of Restricted Boltzmann Machines (RBM) and Multi-Layer Perceptron (MLP) that regenerates the most likely layout. In the domain of architectural modeling, there is geometric multi-model fitting. We introduce a novel guided sampling algorithm based on Minimum Spanning Trees (MST), which surpasses other propagation techniques in terms of robustness to noise. We make a number of additional contributions such as measure of model deviation which captures variations among fitted models

Discovery and Atmospheric Characterization of Giant Planet Kepler-12b: An Inflated Radius Outlier

We report the discovery of planet Kepler-12b (KOI-20), which at 1.695 ± 0.030 R_J is among the handful of planets with super-inflated radii above 1.65 R_J. Orbiting its slightly evolved G0 host with a 4.438 day period, this 0.431 ± 0.041 M_J planet is the least irradiated within this largest-planet-radius group, which has important implications for planetary physics. The planet's inflated radius and low mass lead to a very low density of 0.111 ± 0.010 g cm^(–3). We detect the occultation of the planet at a significance of 3.7σ in the Kepler bandpass. This yields a geometric albedo of 0.14 ± 0.04; the planetary flux is due to a combination of scattered light and emitted thermal flux. We use multiple observations with Warm Spitzer to detect the occultation at 7σ and 4σ in the 3.6 and 4.5 μm bandpasses, respectively. The occultation photometry timing is consistent with a circular orbit at e < 0.01 (1σ) and e < 0.09 (3σ). The occultation detections across the three bands favor an atmospheric model with no dayside temperature inversion. The Kepler occultation detection provides significant leverage, but conclusions regarding temperature structure are preliminary, given our ignorance of opacity sources at optical wavelengths in hot Jupiter atmospheres. If Kepler-12b and HD 209458b, which intercept similar incident stellar fluxes, have the same heavy-element masses, the interior energy source needed to explain the large radius of Kepler-12b is three times larger than that of HD 209458b. This may suggest that more than one radius-inflation mechanism is at work for Kepler-12b or that it is less heavy-element rich than other transiting planets

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

Energy Based Multi-Model Fitting and Matching Problems

Feature matching and model fitting are fundamental problems in multi-view geometry. They are chicken-&-egg problems: if models are known it is easier to find matches and vice versa. Standard multi-view geometry techniques sequentially solve feature matching and model fitting as two independent problems after making fairly restrictive assumptions. For example, matching methods rely on strong discriminative power of feature descriptors, which fail for stereo images with repetitive textures or wide baseline. Also, model fitting methods assume given feature matches, which are not known a priori. Moreover, when data supports multiple models the fitting problem becomes challenging even with known matches and current methods commonly use heuristics. One of the main contributions of this thesis is a joint formulation of fitting and matching problems. We are first to introduce an objective function combining both matching and multi-model estimation. We also propose an approximation algorithm for the corresponding NP-hard optimization problem using block-coordinate descent with respect to matching and model fitting variables. For fixed models, our method uses min-cost-max-flow based algorithm to solve a generalization of a linear assignment problem with label cost (sparsity constraint). Fixed matching case reduces to multi-model fitting subproblem, which is interesting in its own right. In contrast to standard heuristic approaches, we introduce global objective functions for multi-model fitting using various forms of regularization (spatial smoothness and sparsity) and propose a graph-cut based optimization algorithm, PEaRL. Experimental results show that our proposed mathematical formulations and optimization algorithms improve the accuracy and robustness of model estimation over the state-of-the-art in computer vision

A blinded determination of H0H_0 from low-redshift Type Ia supernovae, calibrated by Cepheid variables

Presently a ${>}3\sigma$ tension exists between values of the Hubble constant $H_0$ derived from analysis of fluctuations in the Cosmic Microwave Background by Planck, and local measurements of the expansion using calibrators of type Ia supernovae (SNe Ia). We perform a blinded reanalysis of Riess et al. 2011 to measure $H_0$ from low-redshift SNe Ia, calibrated by Cepheid variables and geometric distances including to NGC 4258. This paper is a demonstration of techniques to be applied to the Riess et at. 2016 data. Our end-to-end analysis starts from available CfA3 and LOSS photometry, providing an independent validation of Riess et al. 2011. We obscure the value of $H_0$ throughout our analysis and the first stage of the referee process, because calibration of SNe Ia requires a series of often subtle choices, and the potential for results to be affected by human bias is significant. Our analysis departs from that of Riess et al. 2011 by incorporating the covariance matrix method adopted in SNLS and JLA to quantify SN Ia systematics, and by including a simultaneous fit of all SN Ia and Cepheid data. We find $H_0 = 72.5 \pm 3.1$ (stat) $\pm 0.77$ (sys) km s$^{-1}$ Mpc$^{-1}$ with a three-galaxy (NGC 4258+LMC+MW) anchor. The relative uncertainties are 4.3% statistical, 1.1% systematic, and 4.4% total, larger than in Riess et al. 2011 (3.3% total) and the Efstathiou 2014 reanalysis (3.4% total). Our error budget for $H_0$ is dominated by statistical errors due to the small size of the supernova sample, whilst the systematic contribution is dominated by variation in the Cepheid fits, and for the SNe Ia, uncertainties in the host galaxy mass dependence and Malmquist bias.Comment: 38 pages, 13 figures, 13 tables; accepted for publication in MNRA