Jlinkage:
Robust fitting of multiple models
R. Toldo, A. Fusiello
Overview
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 Jlinkage, is used to group points belonging to
the same model. The method does not require prior specification of
the number of models, nor it necessitate parameters tuning.
Experimental results demonstrate the superior performances of the
algorithm.
A list of applications of jlinkage.
Method
The method starts with random sampling, as in RANSAC. Then we consider
the preference set of each point, i.e., the set of models that are
satisfied by the point within a tolerance. The characteristic
function of the PS of a point can be regarded as a conceptual
representation of that point. Points belonging to the same structure
will have similar PS, in other words, they will cluster in the
conceptual space. The Jlinkage algorithm is an
agglomerative clustering that proceeds by linking elements with
Jaccard distance
smaller than 1 and stop as soon as there are no such
elements left.
Code
Experiments

Video 
Realtime Jlinkage 

Image consistent patches 
Video 




Reference papers
 Toldo, R. and Fusiello, A. Imageconsistent patches from unstructured points with Jlinkage.
In Image and Vision Computing, 31 (10): 756770, 2013.
PDF
 Toldo, R. and Fusiello, A. Realtime Incremental JLinkage for Robust Multiple
Structures Estimation. In Proceedings of the International Symposium on 3D Data
Processing, Visualization and Transmission (3DPVT), 2010.
PDF
 Toldo, R. and Fusiello, A. Photoconsistent Planar Patches from Unstructured Cloud of Points.
In Proceedings of the European Conference on Computer Vision (ECCV), pages 589602, Springer,
Lecture Notes in Computer Science , 2010.
PDF
 Toldo, R. and Fusiello, A. Robust Multiple Structures Estimation with JLinkage. In
Proceedings of the European Conference on Computer Vision (ECCV), pages 537547, Springer,
Marseille, FR, Lecture Notes in Computer Science 5302, 2008.
PDF
Tlinkage:
A Continuous Relaxation of JLinkage
L. Magri, A. Fusiello
Overview
Tlinkage is an improvement of Jlinkage for fitting multiple instances of a model to nois data corrupted by outliers.
In TLinkage the binary preference analysis implemented by Jlinkage is replaced by a continuous (soft, or fuzzy) generalization.
The benefits of working with continuous values rather than operating with hard thresholding is that we are allowed to integrate more specific information on residual for depicting points preferences (this parallels the difference between RANSAC and MSAC if Consensus Set is considered).
Consequently the soft threshold parameter adopted by TLinkage is a more educated guess compared to the JLinkage hard inlier threshold. Tlinkage also takes advantage of the more expressive representation of points both in term of misclassification error and robustness to outliers.
Code
Experiments
The reference page for MCT  Fitting Multiple Heterogeneous Models by Multiclass Cascaded Tlinkage  is here.
Reference paper
 Magri, L. and Fusiello, A. TLinkage: A Continuous Relaxation of JLinkage for MultiModel Fitting.
In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. (PDF)