802 research outputs found
Robust Rotation Synchronization via Low-rank and Sparse Matrix Decomposition
This paper deals with the rotation synchronization problem, which arises in
global registration of 3D point-sets and in structure from motion. The problem
is formulated in an unprecedented way as a "low-rank and sparse" matrix
decomposition that handles both outliers and missing data. A minimization
strategy, dubbed R-GoDec, is also proposed and evaluated experimentally against
state-of-the-art algorithms on simulated and real data. The results show that
R-GoDec is the fastest among the robust algorithms.Comment: The material contained in this paper is part of a manuscript
submitted to CVI
Robust Linear Regression Analysis - A Greedy Approach
The task of robust linear estimation in the presence of outliers is of
particular importance in signal processing, statistics and machine learning.
Although the problem has been stated a few decades ago and solved using
classical (considered nowadays) methods, recently it has attracted more
attention in the context of sparse modeling, where several notable
contributions have been made. In the present manuscript, a new approach is
considered in the framework of greedy algorithms. The noise is split into two
components: a) the inlier bounded noise and b) the outliers, which are
explicitly modeled by employing sparsity arguments. Based on this scheme, a
novel efficient algorithm (Greedy Algorithm for Robust Denoising - GARD), is
derived. GARD alternates between a least square optimization criterion and an
Orthogonal Matching Pursuit (OMP) selection step that identifies the outliers.
The case where only outliers are present has been studied separately, where
bounds on the \textit{Restricted Isometry Property} guarantee that the recovery
of the signal via GARD is exact. Moreover, theoretical results concerning
convergence as well as the derivation of error bounds in the case of additional
bounded noise are discussed. Finally, we provide extensive simulations, which
demonstrate the comparative advantages of the new technique
Pentagon-Match (PMatch): Identification of View-Invariant Planar Feature for Local Feature Matching-Based Homography Estimation
In computer vision, finding correct point correspondence among images plays
an important role in many applications, such as image stitching, image
retrieval, visual localization, etc. Most of the research works focus on the
matching of local feature before a sampling method is employed, such as RANSAC,
to verify initial matching results via repeated fitting of certain global
transformation among the images. However, incorrect matches may still exist.
Thus, a novel sampling scheme, Pentagon-Match (PMatch), is proposed in this
work to verify the correctness of initially matched keypoints using pentagons
randomly sampled from them. By ensuring shape and location of these pentagons
are view-invariant with various evaluations of cross-ratio (CR), incorrect
matches of keypoint can be identified easily with homography estimated from
correctly matched pentagons. Experimental results show that highly accurate
estimation of homography can be obtained efficiently for planar scenes of the
HPatches dataset, based on keypoint matching results provided by LoFTR.
Besides, accurate outlier identification for the above matching results and
possible extension of the approach for multi-plane situation are also
demonstrated.Comment: arXiv admin note: text overlap with arXiv:2211.0300
Multiple structure recovery via robust preference analysis
2noThis paper address the extraction of multiple models from outlier-contaminated data by exploiting preference analysis and low rank approximation. First points are represented in the preference space, then Robust PCA (Principal Component Analysis) and Symmetric NMF (Non negative Matrix Factorization) are used to break the multi-model fitting problem into many single-model problems, which in turn are tackled with an approach inspired to MSAC (M-estimator SAmple Consensus) coupled with a model-specific scale estimate. Experimental validation on public, real data-sets demonstrates that our method compares favorably with the state of the art.openopenMagri, Luca; Fusiello, AndreaMagri, Luca; Fusiello, Andre
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