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

Magri, Luca; Fusiello, Andrea

doi:10.1109/cvpr.2014.505

Cited by 133 publications

(221 citation statements)

References 26 publications

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“…Finally, please note that if k is not available, a practical solution is to solve Set Cover and detect outliers a-posteriori as points belonging to structures with the smallest consensus sets, as in J-linkage [15].…”

Section: Maximum Coverage Formulationmentioning

confidence: 99%

“…In this section we investigate the performance of ILPRansaCov with respect to Greedy-RansaCov, which emulates Sequential Ransac, J-Linkage [8] and T-Linkage [15] on synthetic data, using the same sampling and the same inlier threshold for all the methods (or, equivalently, the same P matrix). We obtained the implementations of J-Linkage and T-Linkage from [39].…”

Section: Experiments On Simulated Datamentioning

confidence: 99%

“…In all these scenarios we compare ILP-RansaCov with JLinkage [8], T-linkage [15] and RPA [17], whose implementation is taken from [40]. In addition, one reference method have been added to the comparison for each specific scenario, namely: MFIGP [38] in the vanishing point experiments, SSC [41] for video motion segmentation and RCMSA [18] for two-views segmentation.…”

Section: Experimental Comparison On Real Datamentioning

confidence: 99%

“…Structures are recognized as cluster of points in this preference space therefore the emphasis is shifted from the estimation to the segmentation part of the problem. T-Linkage [15] extends this idea by using soft votes to depict points preference more accurately.…”

Section: Preference Analysismentioning

confidence: 99%

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Multiple structure recovery with maximum coverage

Magri

Fusiello

2017

Machine Vision and Applications

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We present a general framework for geometric model fitting based on a set coverage formulation, that caters for intersecting structures and outliers in a simple and principled manner. The multi-model fitting problem is formulated in terms of the optimization of a consensus-based global cost function, which allows to sidestep the pitfalls of preference approaches based on clustering and to avoid the difficult trade-off between data fidelity and complexity of other optimization formulations. Two especially appealing characteristics of this method are the ease with which it can be implemented and its modularity with respect to the solver and to the sampling strategy. Few intelligible parameters need to be set and tuned, namely the inlier threshold and the number of desired models. The summary of the experiments is that our method compares favourably with its competitors overall, and it is always either the best performer or almost on par with the best performer in specific scenarios.

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Section: Maximum Coverage Formulationmentioning

confidence: 99%

Section: Experiments On Simulated Datamentioning

confidence: 99%

Section: Experimental Comparison On Real Datamentioning

confidence: 99%

Section: Preference Analysismentioning

confidence: 99%

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Multiple structure recovery with maximum coverage

Magri

Fusiello

2017

Machine Vision and Applications

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“…In this work, we use the MW assumption to condition T-linkage [22], a clustering technique that builds on the original J-linkage [29] algorithm. Unlike traditional sequential RANSAC approaches that find one structure at a time (where each structure is associated with exactly one model), J-and T-linkage cluster points that are associated to possibly multiple similar models.…”

Section: Introductionmentioning

confidence: 99%

Multi-planar Fitting in an Indoor ManhattanWorld

Kim

Manduchi

2017

2017 IEEE Winter Conference on Applications of Computer Vision (WACV)

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We present an algorithm that finds planar structures in a Manhattan world from two pictures taken from different viewpoints with unknown baseline. The Manhattan world assumption constrains the homographies induced by the visible planes on the image pair, thus enabling robust reconstruction. We extend the T-linkage algorithm for multistructure discovery to account for constrained homographies, and introduce algorithms for sample point selection and orientation-preserving cluster merging. Results are presented on three indoor data set, showing the benefit of the proposed constraints and algorithms.

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Multi-class Model Fitting by Energy Minimization and Mode-Seeking

Baráth

Matas

2018

Computer Vision – ECCV 2018

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We propose a general formulation, called Multi-X, for multi-class multi-instance model fitting -the problem of interpreting the input data as a mixture of noisy observations originating from multiple instances of multiple classes. We extend the commonly used α-expansion-based technique with a new move in the label space. The move replaces a set of labels with the corresponding density mode in the model parameter domain, thus achieving fast and robust optimization. Key optimization parameters like the bandwidth of the mode seeking are set automatically within the algorithm. Considering that a group of outliers may form spatially coherent structures in the data, we propose a crossvalidation-based technique removing statistically insignificant instances. Multi-X outperforms significantly the stateof-the-art on publicly available datasets for diverse problems: multiple plane and rigid motion detection; motion segmentation; simultaneous plane and cylinder fitting; circle and line fitting.

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

Cited by 133 publications

References 26 publications

Multiple structure recovery with maximum coverage

Multiple structure recovery with maximum coverage

Multi-planar Fitting in an Indoor ManhattanWorld

Multi-class Model Fitting by Energy Minimization and Mode-Seeking

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