Robust principal component analysis for computer vision

By Fernando De la Torre, Michael J. Black. Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, 2001

445 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Computer Vision and Pattern Recognition, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/abb3415117cedf1bcf62b86867dfa14859277455

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In Civil and Structural Engineering

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Citations in the corpus

Motion Segmentation in the Presence of Outlying, Incomplete, or Corrupted Trajectories (2010, methodology): “These kinds of trajectories have been referred to as “sample outliers” by [ 7 ], suggesting that no subset of the trajectory corresponds to any affine motion model.”
Learning to Dress 3D People in Generative Clothing (2019, methodology): “ntrol over pose and clothing types, and can be extended to control other factors. Generative models for 3D meshes. Generative models for 3D shapes are usually based on PCA [50] or its robust versions [51]. Alternately, deep learning methods such as Variational Autoencoders (VAE) [20] and Generative ”
A two-stage linear discriminant analysis via QR-decomposition (2005, methodology): “It has been used widely in many applications such as face recognition [2], [19], [21], [27], text classification [4], [11], [32], microarray data classification [7], etc. Classical LDA aims to find an optimal transformation by minimizing the within-class distance and maximizing the between-class…”
A comparative study on diabetes disease diagnosis using neural networks (2009, methodology): “One drawback of typical PCA methods is that they are least squares estimation techniques and hence fail to account for outliers and atypical values which are common in realistic training and test sets (Torre & Black, 2001).”
Analysis of Rain and Snow in Frequency Space (2008, methodology): “In addition to layer extraction, techniques such as Gaussian mixture models (Stauffer and Grimson 1998), kernel density estimation (Elgammal et al. 2000), or robust PCA (de la Torre and Black 2001) could model the variation caused by rain and snow more effectively.”
Robust Computation of Linear Models by Convex Relaxation (2012, background): “4 Screening for Outliers Another common approach is to remove possible outliers and then estimate the underlying subspace by PCA [15,66,67].”
Multiframe Motion Segmentation with Missing Data Using PowerFactorization and GPCA (2008, unknown): “Similar (not identical) alternation algorithms have been proposed in (Shum et al. 1995; De la Torre and Black 2001; Buchanan and Fitzgibbon 2000).”
Segmenting foreground objects from a dynamic textured background via a robust Kalman filter (2003, methodology): “The foreground object regions are then obtained by thresholding the weighting function used in the robust Kalman lter.”
A Framework for Robust Subspace Learning (2003, methodology): “results of this work have been presented in De la Torre and Black (2001).”