Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Compact Representations of Videos Through Dominant and Multiple Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct incremental model-based image motion segmentation for video analysis
Signal Processing - Video segmentation for content-based processing manipulation
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Frame Estimation of Planar Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Tracking with Bayesian Estimation of Dynamic Layer Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principal Component Analysis with Missing Data and Its Application to Polyhedral Object Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
MINPRAN: A New Robust Estimator for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting and Tracking Multiple Moving Objects Using Temporal Integration
ECCV '92 Proceedings of the Second European Conference on Computer Vision
A Framework for Robust Subspace Learning
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
A Layered Approach to Stereo Reconstruction
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A Robust Subspace Approach to Layer Extraction
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
An Integrated Bayesian Approach to Layer Extraction from Image Sequences
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Motion Segmentation and Tracking Using Normalized Cuts
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A robust subspace approach to extracting layers from image sequences
A robust subspace approach to extracting layers from image sequences
Motion layer extraction in the presence of occlusion using graph cut
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A layered video object coding system using sprite and affine motion model
IEEE Transactions on Circuits and Systems for Video Technology
Robust subspace analysis for detecting visual attention regions in images
Proceedings of the 13th annual ACM international conference on Multimedia
Cluster-based pattern discrimination: A novel technique for feature selection
Pattern Recognition Letters
Localized feature selection for clustering
Pattern Recognition Letters
Detection of visual attention regions in images using robust subspace analysis
Journal of Visual Communication and Image Representation
Interactive video layer decomposition and matting
ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
Expert Systems with Applications: An International Journal
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Subspace clustering has many applications in computer vision, such as image/video segmentation and pattern classification. The major issue in subspace clustering is to obtain the most appropriate subspace from the given noisy data. Typical methods (e.g., SVD, PCA, and Eigendecomposition) use least squares techniques, and are sensitive to outliers. In this paper, we present the k-th Nearest Neighbor Distance (kNND) metric, which, without actually clustering the data, can exploit the intrinsic data cluster structure to detect and remove influential outliers as well as small data clusters. The remaining data provide a good initial inlier data set that resides in a linear subspace whose rank (dimension) is upper-bounded. Such linear subspace constraint can then be exploited by simple algorithms, such as iterative SVD algorithm, to (1) detect the remaining outliers that violate the correlation structure enforced by the low rank subspace, and (2) reliably compute the subspace. As an example, we apply our method to extracting layers from image sequences containing dynamically moving objects.