Image-consistent patches from unstructured points with J-linkage

Toldo, Roberto; Fusiello, Andrea

doi:10.1016/j.imavis.2013.07.007

Cited by 12 publications

(11 citation statements)

References 41 publications

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“…x∈U PS (x). (5) and the distance between clusters is computed as the Jaccard distance [22] between the respective preference representations. The Jaccard distance between two sets A, B is defined as…”

Section: Preference Analysismentioning

confidence: 99%

“…A typical example of this problem can be found in 3D reconstruction, where multi-model fitting is employed either to estimate multiple rigid moving objects and hence to initialize multi-body Structure from Motion [1,2], or to produce intermediate geometric interpretations of reconstructed 3D point cloud by fitting geometric primitives [3,4,5]. Other scenarios in which the estimation of multiple geometric structure plays a primary role include face clustering, body-pose estimation, augmented reality, image stitching and video motion segmentation, to name just a few.…”

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confidence: 99%

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Multiple structure recovery with T-linkage

Magri

Fusiello

2017

Journal of Visual Communication and Image Representation

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This 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-ofthe-art methods on public domain real datasets.

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Section: Preference Analysismentioning

confidence: 99%

mentioning

confidence: 99%

Multiple structure recovery with T-linkage

Magri

Fusiello

2017

Journal of Visual Communication and Image Representation

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“…Building on this idea, the J-Linkage algorithm [8,13] was the first successful application of a preference-based representation of data. A two steps first-represent-thencluster scheme is implemented: data are represented by the votes they grant to a set of model hypotheses, then a greedy agglomerative clustering is performed to obtain a partition of the data.…”

Section: Preference Analysismentioning

confidence: 99%

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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“…Hence, the preference analysis-based method first generates a large number of hypotheses by sampling a minimum sample set (MSS), and then performs preference analysis on the hypotheses residuals. The most classical method, J-linkage [20,21,33], adopts the binarized conceptual preference of points, which binarizes the residuals by inlier threshold, and introduces the Jaccard distance to conduct linkage clustering of the point preferences, thus the inliers are segmented into different clusters. Similar to J-linkage, T-linkage [22,24,25] uses relaxation of the binary preference function and the soft Tanimoto distance to improve the conceptual preference in J-linkage for better clustering.…”

Section: Introductionmentioning

confidence: 99%

Outlier Detection Based on Residual Histogram Preference for Geometric Multi-Model Fitting

Zhao

Zhang

Xie

et al. 2020

Sensors

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Geometric model fitting is a fundamental issue in computer vision, and the fitting accuracy is affected by outliers. In order to eliminate the impact of the outliers, the inlier threshold or scale estimator is usually adopted. However, a single inlier threshold cannot satisfy multiple models in the data, and scale estimators with a certain noise distribution model work poorly in geometric model fitting. It can be observed that the residuals of outliers are big for all true models in the data, which makes the consensus of the outliers. Based on this observation, we propose a preference analysis method based on residual histograms to study the outlier consensus for outlier detection in this paper. We have found that the outlier consensus makes the outliers gather away from the inliers on the designed residual histogram preference space, which is quite convenient to separate outliers from inliers through linkage clustering. After the outliers are detected and removed, a linkage clustering with permutation preference is introduced to segment the inliers. In addition, in order to make the linkage clustering process stable and robust, an alternative sampling and clustering framework is proposed in both the outlier detection and inlier segmentation processes. The experimental results also show that the outlier detection scheme based on residual histogram preference can detect most of the outliers in the data sets, and the fitting results are better than most of the state-of-the-art methods in geometric multi-model fitting.

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Image-consistent patches from unstructured points with J-linkage

Cited by 12 publications

References 41 publications

Multiple structure recovery with T-linkage

Multiple structure recovery with T-linkage

Multiple structure recovery with maximum coverage

Outlier Detection Based on Residual Histogram Preference for Geometric Multi-Model Fitting

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