Simultaneous Rectification and Alignment via Robust Recovery of Low-rank Tensors
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Summary
This work proposes a general method for recovering low-rank three-order tensors, in which the data can be deformed by some unknown transformation and corrupted by arbitrary sparse errors.
- Type
- article
- Published
- 2013-12-05
- Cited by
- 28
- References
- 16
- OpenAlex
- https://openalex.org/W2103053668
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:10166406
Keywords
Tensor (intrinsic definition), Computer science, Minification, Algorithm, Convergence (economics)
References
- Least squares congealing for unsupervised alignment of images
- Estimation of low-rank tensors via convex optimization
- A collaborative framework for 3D alignment and classification of heterogeneous subvolumes in cryo-electron tomography
- RASL: Robust alignment by sparse and low-rank decomposition for linearly correlated images
- Tensor Decompositions and Applications
- Three-way arrays: rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics
- Tensor completion and low-n-rank tensor recovery via convex optimization
- Linearized augmented Lagrangian and alternating direction methods for nuclear norm minimization
- Tensor Completion for Estimating Missing Values in Visual Data
- Data driven image models through continuous joint alignment
- Robust principal component analysis?
- Joint data alignment up to (lossy) transformations
- TILT: Transform Invariant Low-Rank Textures
- Unsupervised Joint Alignment of Complex Images
- Nuclear Norms for Tensors and Their Use for Convex Multilinear Estimation
- Optimum Subspace Learning and Error Correction for Tensors
Cited by
- Gait recognition method for arbitrary straight walking paths using appearance conversion machine
- Low-Rank Tensor Learning with Discriminant Analysis for Action Classification and Image Recovery
- Fast randomized Singular Value Thresholding for Nuclear Norm Minimization
- New Design Criteria for Robust PCA and a Compliant Bayesian-Inspired Algorithm
- Nonconvex plus quadratic penalized low-rank and sparse decomposition for noisy image alignment
- Semi-Supervised Representation Learning based on Probabilistic Labeling
- Robust Low-Rank Tensor Recovery With Regularized Redescending M-Estimator
- Fast Randomized Singular Value Thresholding for Low-Rank Optimization
- Low-rank tensor learning for human action recognition
- Learning compact binary codes from higher-order tensors via Free-Form Reshaping and Binarized Multilinear PCA
- Semi-Supervised Dictionary Learning via Structural Sparse Preserving
- Manifold Constrained Low-Rank Decomposition
- Modeling Sparse Deviations for Compressed Sensing using Generative Models
- Online Robust Low-Rank Tensor Modeling for Streaming Data Analysis
- Robust Low-Rank Tensor Recovery with Rectification and Alignment
- Convolutional Imputation of Matrix Networks
- Video Rain/Snow Removal by Transformed Online Multiscale Convolutional Sparse Coding
- Tensor recovery from noisy and multi-level quantized measurements
- Online Rain/Snow Removal From Surveillance Videos
- Robust Low Transformed Multi-Rank Tensor Methods for Image Alignment
Related papers
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- Tensor Decompositions and Applications
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- A collaborative framework for 3D alignment and classification of heterogeneous subvolumes in cryo-electron tomography
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- MPCA: Multilinear Principal Component Analysis of Tensor Objects
- Some mathematical notes on three-mode factor analysis
- A Generalized Low-Rank Appearance Model for Spatio-temporally Correlated Rain Streaks