A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Robust Visual Tracking via Implicit Low-Rank Constraints and Structural Color Histograms
[article]
2019
arXiv
pre-print
Preserved Fulltext
The experimental results on standard benchmarks demonstrate that our Implicit Low-Rank Constraints and Structural Color Histograms (ILRCSCH) tracker outperforms state-of-the-art methods. ...
To encourage temporal continuity and to explore the smooth variation of target appearance, we propose to enhance low-rank structure of the learned filters, which can be realized by constraining the successive ...
In this work, we propose Implicit Low-Rank Constraints and Structural Color Histograms (ILRCSCH), which is based on three handcrafted features (HOG, CN [17] , and Structural Color Histogram) in order ...
arXiv:1912.11343v1
fatcat:3pb25zcxibe7rct4tv7ty7l7fy
Curvilinear Structure Tracking by Low Rank Tensor Approximation with Model Propagation
2014
2014 IEEE Conference on Computer Vision and Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is application/pdf.
Tracking of such anatomical structures and devices is very challenging due to large degrees of appearance changes, low visibility of X-ray images and the deformable nature of the underlying motion field ...
Specifically, the deformable tracking is formulated as a multi-dimensional assignment problem which is solved by rank-1 1 tensor approximation. ...
Low visibility and poor image quality due to a low dose of radiations in interventional imaging make the deformable tracking more difficult. ...
doi:10.1109/cvpr.2014.391
dblp:conf/cvpr/ChengPZYL14
fatcat:vmym364t5bdspfpaknv635jxlu
A Simplified Low Rank and Sparse Model for Visual Tracking
2017
Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Sparsity always pursues a sparse enough solution that ignores the low-rank structure and vice versa. Therefore, this paper replaces the low-rank and sparse constraints with 2,1 l norm. ...
Numerous tracking methods using low-rank and sparse constraints perform well in visual tracking. However, these methods cannot reasonably balance the two characteristics. ...
the low-rank structure of Z. ...
doi:10.5220/0006117003010308
dblp:conf/icpram/WangXLXZ17
fatcat:xedfnx5ktjc7thuusfmegomtmi
Better Feature Tracking through Subspace Constraints
2014
2014 IEEE Conference on Computer Vision and Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Our approach does not require direct modeling of the structure or the motion of the scene, and runs in real time on a single CPU core. ...
While this approach works quite well when dealing with high-quality video and "strong" features, it often falters when faced with dark and noisy video containing low-quality features. ...
Low Rank Regularization Framework If we want to encourage a low rank structure in the trajectories, we cannot view the tracking of different features as separate problems. ...
doi:10.1109/cvpr.2014.441
dblp:conf/cvpr/PolingLS14
fatcat:jaiksn3ibfdtdfyujlagb23zta
Page 803 of Journal of Sedimentary Petrology Vol. 45, Issue 4
[page]
1975
Journal of Sedimentary Petrology
Preserved Fulltext
The Internet Archive has digitized a microfilm copy of this work. It may be possible to borrow a copy for reading.
—Structural prisms arranged according to rank from flutes (low) to oscillation ripples (high). Each point designates an outcrop containing the structure. ...
For our pur- poses the former is referred to as low rank and latter as high rank.
As expected, the sedimentary structures show a definite relationship with lithology. ...
Background subtraction in high-contrast imaging
2020
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
• usually not a major effort if derotator can be accessed
LESSON #2: LOW-RANK APPROXIMATIONS
๏ Efficient way to disentangle rotating field from quasi-static
background features (in pupil tracking) ...
๏ Most appropriate for point sources -known to affect images
of extended sources
• self-subtraction: source partly captured in low-rank subspace
• over-subtraction: source projection onto low-rank ...
doi:10.5281/zenodo.4249860
fatcat:ceyousg2ife7xhjei3m2noiuwm
Low-Rank Representation with Graph Constraints for Robust Visual Tracking
2017
IEICE transactions on information and systems
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
In this paper, we propose a novel subspace representation algorithm for robust visual tracking by using low-rank representation with graph constraints (LRGC). ...
In this paper, we aim to exploit benefits from both low-rank representation and graph constraint, and deploy it to handle challenging visual tracking problems. ...
CLRST represents particles as sparse linear combinations of dictionary templates to capture the low-rank structure of data. ...
doi:10.1587/transinf.2016edp7422
fatcat:uxfvwz2kqzcfbcvvbx2of2jzca
Robust visual tracking via efficient manifold ranking with low-dimensional compressive features
2015
Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Due to the outstanding ability of the manifold ranking algorithm in discovering the underlying geometrical structure of a given image database, our tracker is more robust to overcome tracking drift. ...
Meanwhile, we adopt non-adaptive random projections to preserve the structure of original image space, and a very sparse measurement matrix is used to efficiently extract low-dimensional compressive features ...
Due to a low complexity for computing the ranking function r * , we can reconstruct graph in each tracking round efficiently. ...
doi:10.1016/j.patcog.2015.03.008
fatcat:ieosuimeqrheveimktp3xb6rqq
Low-Rank Representation-Based Object Tracking Using Multitask Feature Learning with Joint Sparsity
2014
Abstract and Applied Analysis
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
We address object tracking problem as a multitask feature learning process based on low-rank representation of features with joint sparsity. ...
Since the proposed method attempts to perform the feature learning problem in both multitask and low-rank manner, it can not only reduce the dimension but also improve the tracking performance without ...
The low-rank sparse tracking (LRST) was proposed by representing all samples using only a few templates [17] . ...
doi:10.1155/2014/147353
fatcat:dwuh6xpzmrahpmr5ki7uzut334
Visual tracking via graph-based efficient manifold ranking with low-dimensional compressive features
2014
2014 IEEE International Conference on Multimedia and Expo (ICME)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
In this paper, a novel and robust tracking method based on efficient manifold ranking is proposed. ...
Meanwhile, we adopt non-adaptive random projections to preserve the structure of original image space, and a very sparse measurement matrix is used to efficiently extract low-dimensional compressive features ...
Due to a low complexity for computing the ranking function r * , we can reconstruct graph in each tracking round efficiently. ...
doi:10.1109/icme.2014.6890194
dblp:conf/icmcs/ZhouHXFZY14
fatcat:bipafeenofbatla47sfti3gs6y
Moving Object Detection in Dynamic Environment via Weighted Low-Rank Structured Sparse RPCA and Kalman Filtering
2022
Mathematical Problems in Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Besides, the weighted low-rank and structured sparse RPCA model is efficiently solved by the Alternating Direction Method of Multipliers (ADMM) optimization algorithm. ...
Therefore, a real-time MOD framework (LSRPCA_KF) is proposed for the dynamic background, where a weighted low-rank and structured sparse RPCA algorithm is used to achieve background modeling for history ...
Offline Analysis of Weighted Low-Rank and Structured Sparsity RPCA-Based Object Detection. ...
doi:10.1155/2022/7087130
doaj:8b5645a158854e57aacf5538c4416676
fatcat:qopjf5mv2re7lpclhf4lrrv3ly
Covariance tracking from sketches of rapid data streams
2015
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Estimating and tracking the covariance matrix of high-dimensional data streams with low complexities in acquisition, storage and computation are of great interest in modern data-intensive applications. ...
In particular, we devise a discounting mechanism in the aggregation procedure to enable faster tracking when the covariance structure changes over time. ...
structures such as sparsity and low rank. ...
doi:10.1109/icassp.2015.7179017
dblp:conf/icassp/JiangC15
fatcat:lj2bjv4yqnfz5avbaymxewaifq
Robust Object Tracking via Reverse Low-Rank Sparse Learning and Fractional-Order Variation Regularization
2020
Mathematical Problems in Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this paper, we propose a novel tracking algorithm via reverse low-rank sparse learning and fractional-order variation regularization. ...
Object tracking based on low-rank sparse learning usually makes the drift phenomenon occur when the target faces severe occlusion and fast motion. ...
Low-rank constraint [1] [2] [3] [4] on the candidate particles can reflect the subspace structure feature of the object appearance. is subspace representation is robust to handle the global appearance ...
doi:10.1155/2020/8640724
fatcat:6kkkuj2mqbgvzdq2uduljocrhm
Non-Rigid Structure-From-Motion With Uniqueness Constraint and Low Rank Matrix Fitting Factorization
2014
IEEE transactions on multimedia
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Index Terms Non-rigid structure-from-motion, uniqueness constraint, low rank matrix fitting, least squares estimation. ...
A framework for occluded and incomplete measured data is also proposed using low rank matrix fitting which is a robust factorization scheme for the matrix completion problem. ...
(SVD) into two low-rank matricesM 2F ×3K ,Ŝ 3K×P and is defined as, W = UΣV T = (UΣ 1 2 )(UΣ 1 2 ) =dot(M 2F ×3KŜ3K×P ), so this model eventually decomposes W as the product of two low-rank matricesM ...
doi:10.1109/tmm.2014.2308415
fatcat:f7qhbozgprbkjezvzfoymyzvpe
UCD IIRG at TREC 2011 Medical Track
2011
Text Retrieval Conference
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
For both these approaches query expansion and concept re-ranking are applied. ...
and developing a method to create a ranking of visits given the retrieved reports. ...
This method of calculating the relevancy score for a visit reduces the impact of very low ranking reports while maintaining the importance of those with high ranks. ...
dblp:conf/trec/CogleySDC11
fatcat:72jaaicgc5bvdlctlwr3a674ty
« Previous
Showing results 1 — 15 out of 365,297 results