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Computer Vision and Image Processing for Automated Surveillance

Abstract: 

Presentation slides covering:

- robust foreground detection / background subtraction via patch-based analysis
- person re-identification based on representations on Riemannian manifolds
- robust object tracking via Grassmann manifolds
- adapting the lessons from big data to computer vision
- future paradigm shifts: computer vision based on networks of neurosynaptic cores

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1 user has voted: Conrad Sanderson

Paper Details

Authors:
Submitted On:
23 February 2016 - 1:43pm
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Type:
Presentation Slides

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Sanderson_Computer_Vision_and_Image_Processing_for_Automated_Surveillance.pdf

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[1] , "Computer Vision and Image Processing for Automated Surveillance", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/202. Accessed: Oct. 18, 2017.
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url = {http://sigport.org/202},
author = { },
publisher = {IEEE SigPort},
title = {Computer Vision and Image Processing for Automated Surveillance},
year = {2015} }
TY - EJOUR
T1 - Computer Vision and Image Processing for Automated Surveillance
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PY - 2015
PB - IEEE SigPort
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. (2015). Computer Vision and Image Processing for Automated Surveillance. IEEE SigPort. http://sigport.org/202
, 2015. Computer Vision and Image Processing for Automated Surveillance. Available at: http://sigport.org/202.
. (2015). "Computer Vision and Image Processing for Automated Surveillance." Web.
1. . Computer Vision and Image Processing for Automated Surveillance [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/202