Robust Multiple Structures Estimation with J-Linkage

  title={Robust Multiple Structures Estimation with J-Linkage},
  author={Roberto Toldo and Andrea Fusiello},
This paper tackles the problem of fitting multiple instances of a model to data corrupted by noise and outliers. The proposed solution is based on random sampling and conceptual data representation. Each point is represented with the characteristic function of the set of random models that fit the point. A tailored agglomerative clustering, called J-linkage, is used to group points belonging to the same model. The method does not require prior specification of the number of models, nor it… 

Multiple Models Fitting as a Set Coverage Problem

  • L. MagriAndrea Fusiello
  • Computer Science
    2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2016
This paper derives a simple and effective method that generalizes Ransac to multiple models and deals with intersecting structures and outliers in a straightforward and principled manner, while avoiding the typical shortcomings of sequential approaches and those of clustering.

Scale Estimation in Multiple Models Fitting via Consensus Clustering

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Multiple structure recovery with T-linkage

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T-Linkage: A Continuous Relaxation of J-Linkage for Multi-model Fitting

The binary preference analysis implemented by J-linkage is replaced by a continuous (soft, or fuzzy) generalization that proves to perform better than J- linkage on simulated data, and compares favorably with state of the art methods on public domain real datasets.

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Bias in robust estimation caused by discontinuities and multiple structures

  • C. Stewart
  • Mathematics
    IEEE Trans. Pattern Anal. Mach. Intell.
  • 1997
The "pseudo outlier bias" metric is developed using techniques from the robust statistics literature, and it is used to study the error in robust fits caused by distributions modeling various types of discontinuities.

Mean Shift: A Robust Approach Toward Feature Space Analysis

It is proved the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density.

Nonlinear Mean Shift for Clustering over Analytic Manifolds

  • R. SubbaraoP. Meer
  • Mathematics
    2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)
  • 2006
The mean shift algorithm is generalized for clustering on matrix Lie groups and extended to a more general class of nonlinear spaces, the set of analytic manifolds, which is applied to a variety of robust motion segmentation problems and multibody factorization.

Generalized principal component analysis (GPCA)

  • R. VidalYi MaS. Sastry
  • Computer Science, Mathematics
    IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2005
An algebro-geometric solution to the problem of segmenting an unknown number of subspaces of unknown and varying dimensions from sample data points and applications of GPCA to computer vision problems such as face clustering, temporal video segmentation, and 3D motion segmentation from point correspondences in multiple affine views are presented.

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The multiRANSAC algorithm and its application to detect planar homographies

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Detection of Planar Regions with Uncalibrated Stereo using Distributions of Feature Points

This work proposes a robust method for detecting local planar regions in a scene with an uncalibrated stereo based on random sampling using distributions of feature point locations and finds the largest consensus set of the homography.

A new curve detection method: Randomized Hough transform (RHT)

Pattern classification and scene analysis

The topics treated include Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.

Efficient Visualization of Architectural Models from a Structure and Motion Pipeline

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