Pages

Tuesday, May 16, 2017

Thesis: Robust Low-rank and Sparse Decomposition for Moving Object Detection: From Matrices to Tensors by Andrews Cordolino Sobral



Here is what Andrews (whom we have followed for a while now) just sent me (Congratulations Dr. Sobral !)
Hi Igor,

First of all, I would like to congratulate you for your excellent blog.
I would like to share with you my thesis presentation about Robust Low-rank and Sparse Decomposition for Moving Object Detection: From Matrices to Tensors. I think this research work may be of interest to your blog. Please find below the slide presentation and the thesis manuscript:
Thesis presentation (SlideShare):
https://www.slideshare.net/andrewssobral/thesis-presentation-robust-lowrank-and-sparse-decomposition-for-moving-object-detection-from-matrices-to-tensors
Thesis manuscript (ResearchGate):
https://www.researchgate.net/publication/316967304_Robust_Low-rank_and_Sparse_Decomposition_for_Moving_Object_Detection_From_Matrices_to_Tensors
Many thanks,

Andrews Cordolino Sobral
Ph.D. on Computer Vision and Machine Learning
http://andrewssobral.wix.com/home
This thesis introduces the recent advances on decomposition into low-rank plus sparse matrices and tensors, as well as the main contributions to face the principal issues in moving object detection. First, we present an overview of the state-of-the-art methods for low-rank and sparse decomposition, as well as their application to background modeling and foreground segmentation tasks. Next, we address the problem of background model initialization as a reconstruction process from missing/corrupted data. A novel methodology is presented showing an attractive potential for background modeling initialization in video surveillance. Subsequently, we propose a double-constrained version of robust principal component analysis to improve the foreground detection in maritime environments for automated video-surveillance applications. The algorithm makes use of double constraints extracted from spatial saliency maps to enhance object foreground detection in dynamic scenes. We also developed two incremental tensor-based algorithms in order to perform background/foreground separation from multidimensional streaming data. These works address the problem of low-rank and sparse decomposition on tensors. Finally, we present a particular work realized in conjunction with the Computer Vision Center (CVC) at Autonomous University of Barcelona (UAB).




Join the CompressiveSensing subreddit or the Google+ Community or the Facebook page and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.

No comments:

Post a Comment