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Image/Video Processing

A Multi-Frame Optical Flow Spot Tracker


Stationary Motion Estimation

Accurate and robust spot tracking is a necessary tool for quantitative motion analysis in fluorescence microscopy images. In this work, we exploits the underlying stationary motion in biological systems, e.g. the movement of crowds, bacteria swarming and cyclosis in plant cells, and then propose a multi-frame optical flow based tracker. We obtain the stationary motion by adapting a recent optical flow algorithm that relates one image to another locally using an all-pass filter.

Paper Details

Authors:
Christopher Gilliam, Thierry Blu
Submitted On:
23 February 2016 - 1:38pm
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ICIP2015_poster_SLAPTracker.pdf

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[1] Christopher Gilliam, Thierry Blu, "A Multi-Frame Optical Flow Spot Tracker", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/225. Accessed: Jan. 16, 2019.
@article{225-15,
url = {http://sigport.org/225},
author = {Christopher Gilliam; Thierry Blu },
publisher = {IEEE SigPort},
title = {A Multi-Frame Optical Flow Spot Tracker},
year = {2015} }
TY - EJOUR
T1 - A Multi-Frame Optical Flow Spot Tracker
AU - Christopher Gilliam; Thierry Blu
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/225
ER -
Christopher Gilliam, Thierry Blu. (2015). A Multi-Frame Optical Flow Spot Tracker. IEEE SigPort. http://sigport.org/225
Christopher Gilliam, Thierry Blu, 2015. A Multi-Frame Optical Flow Spot Tracker. Available at: http://sigport.org/225.
Christopher Gilliam, Thierry Blu. (2015). "A Multi-Frame Optical Flow Spot Tracker." Web.
1. Christopher Gilliam, Thierry Blu. A Multi-Frame Optical Flow Spot Tracker [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/225

Three-dimensional Reconstruction from Heterogeneous Video Devices With Camera-In-View Information


In this work, a 3D modelization of the surrounding environment is enabled with an improvised ad-hoc camera networks of both static and mobile devices (cloud vision network).

poster.pdf

PDF icon poster.pdf (1055 downloads)

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Submitted On:
23 February 2016 - 1:38pm
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poster.pdf

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[1] , "Three-dimensional Reconstruction from Heterogeneous Video Devices With Camera-In-View Information", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/224. Accessed: Jan. 16, 2019.
@article{224-15,
url = {http://sigport.org/224},
author = { },
publisher = {IEEE SigPort},
title = {Three-dimensional Reconstruction from Heterogeneous Video Devices With Camera-In-View Information},
year = {2015} }
TY - EJOUR
T1 - Three-dimensional Reconstruction from Heterogeneous Video Devices With Camera-In-View Information
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/224
ER -
. (2015). Three-dimensional Reconstruction from Heterogeneous Video Devices With Camera-In-View Information. IEEE SigPort. http://sigport.org/224
, 2015. Three-dimensional Reconstruction from Heterogeneous Video Devices With Camera-In-View Information. Available at: http://sigport.org/224.
. (2015). "Three-dimensional Reconstruction from Heterogeneous Video Devices With Camera-In-View Information." Web.
1. . Three-dimensional Reconstruction from Heterogeneous Video Devices With Camera-In-View Information [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/224

Region-based thresholding using component tree


A gray-level image can be represented by a component tree, based on the inclusion relation of connected regions obtained by threshold decomposition. The great advantage of this structure is the efficient determination of a set of attributes for each component of the image, being widely used in morphological filtering (for instance, area as attribute to the area opening).

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23 February 2016 - 1:43pm
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icip2011_poster.pdf

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[1] , "Region-based thresholding using component tree", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/212. Accessed: Jan. 16, 2019.
@article{212-15,
url = {http://sigport.org/212},
author = { },
publisher = {IEEE SigPort},
title = {Region-based thresholding using component tree},
year = {2015} }
TY - EJOUR
T1 - Region-based thresholding using component tree
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/212
ER -
. (2015). Region-based thresholding using component tree. IEEE SigPort. http://sigport.org/212
, 2015. Region-based thresholding using component tree. Available at: http://sigport.org/212.
. (2015). "Region-based thresholding using component tree." Web.
1. . Region-based thresholding using component tree [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/212

Computer Vision and Image Processing for Automated Surveillance


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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23 February 2016 - 1:43pm
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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: Jan. 16, 2019.
@article{202-15,
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
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/202
ER -
. (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

Object Tracking and Person Re-Identification on Manifolds


person re-identification examples

Slides from the Tutorial on Riemannian Geometry in Computer Vision, presented at the Asian Conference on Computer Vision (ACCV), Singapore, 2014.

The slides show (1) how objects can be interpreted as points on Riemannian and Grassmann manifolds, and (2) various distance measures on manifolds. Demonstrates usefulness of manifold techniques in applications such as object tracking and person re-identification.

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23 February 2016 - 1:43pm
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sanderson_riemannian_geometry_tutorial_slides_accv_2014.pdf

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[1] , "Object Tracking and Person Re-Identification on Manifolds", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/198. Accessed: Jan. 16, 2019.
@article{198-15,
url = {http://sigport.org/198},
author = { },
publisher = {IEEE SigPort},
title = {Object Tracking and Person Re-Identification on Manifolds},
year = {2015} }
TY - EJOUR
T1 - Object Tracking and Person Re-Identification on Manifolds
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/198
ER -
. (2015). Object Tracking and Person Re-Identification on Manifolds. IEEE SigPort. http://sigport.org/198
, 2015. Object Tracking and Person Re-Identification on Manifolds. Available at: http://sigport.org/198.
. (2015). "Object Tracking and Person Re-Identification on Manifolds." Web.
1. . Object Tracking and Person Re-Identification on Manifolds [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/198

A Low-Complexity Algorithm for Static Background Estimation from Cluttered Image Sequences in Surveillance Contexts


For the purposes of foreground estimation, the true background model is unavailable in many practical circumstances and needs to be estimated from cluttered image sequences. We propose a sequential technique for static background estimation in such conditions, with low computational and memory requirements. Image sequences are analysed on a block-by-block basis. For each block location a representative set is maintained which contains distinct blocks obtained along its temporal line.

Paper Details

Authors:
Vikas Reddy, Conrad Sanderson, Brian C. Lovell
Submitted On:
23 February 2016 - 1:43pm
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cluttered_background_estimation.pdf

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[1] Vikas Reddy, Conrad Sanderson, Brian C. Lovell, "A Low-Complexity Algorithm for Static Background Estimation from Cluttered Image Sequences in Surveillance Contexts", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/190. Accessed: Jan. 16, 2019.
@article{190-15,
url = {http://sigport.org/190},
author = {Vikas Reddy; Conrad Sanderson; Brian C. Lovell },
publisher = {IEEE SigPort},
title = {A Low-Complexity Algorithm for Static Background Estimation from Cluttered Image Sequences in Surveillance Contexts},
year = {2015} }
TY - EJOUR
T1 - A Low-Complexity Algorithm for Static Background Estimation from Cluttered Image Sequences in Surveillance Contexts
AU - Vikas Reddy; Conrad Sanderson; Brian C. Lovell
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/190
ER -
Vikas Reddy, Conrad Sanderson, Brian C. Lovell. (2015). A Low-Complexity Algorithm for Static Background Estimation from Cluttered Image Sequences in Surveillance Contexts. IEEE SigPort. http://sigport.org/190
Vikas Reddy, Conrad Sanderson, Brian C. Lovell, 2015. A Low-Complexity Algorithm for Static Background Estimation from Cluttered Image Sequences in Surveillance Contexts. Available at: http://sigport.org/190.
Vikas Reddy, Conrad Sanderson, Brian C. Lovell. (2015). "A Low-Complexity Algorithm for Static Background Estimation from Cluttered Image Sequences in Surveillance Contexts." Web.
1. Vikas Reddy, Conrad Sanderson, Brian C. Lovell. A Low-Complexity Algorithm for Static Background Estimation from Cluttered Image Sequences in Surveillance Contexts [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/190

Bags of Affine Subspaces for Robust Object Tracking


object tracking results

We propose an adaptive tracking algorithm where the object is modelled as a continuously updated bag of affine subspaces, with each subspace constructed from the object's appearance over several consecutive frames. In contrast to linear subspaces, affine subspaces explicitly model the origin of subspaces. Furthermore, instead of using a brittle point-to-subspace distance during the search for the object in a new frame, we propose to use a subspace-to-subspace distance by representing candidate image areas also as affine subspaces.

report.pdf

PDF icon report.pdf (1069 downloads)

Paper Details

Authors:
Sareh Shirazi, Conrad Sanderson, Chris McCool, Mehrtash Harandi
Submitted On:
23 February 2016 - 1:43pm
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report.pdf

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[1] Sareh Shirazi, Conrad Sanderson, Chris McCool, Mehrtash Harandi, "Bags of Affine Subspaces for Robust Object Tracking", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/185. Accessed: Jan. 16, 2019.
@article{185-15,
url = {http://sigport.org/185},
author = {Sareh Shirazi; Conrad Sanderson; Chris McCool; Mehrtash Harandi },
publisher = {IEEE SigPort},
title = {Bags of Affine Subspaces for Robust Object Tracking},
year = {2015} }
TY - EJOUR
T1 - Bags of Affine Subspaces for Robust Object Tracking
AU - Sareh Shirazi; Conrad Sanderson; Chris McCool; Mehrtash Harandi
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/185
ER -
Sareh Shirazi, Conrad Sanderson, Chris McCool, Mehrtash Harandi. (2015). Bags of Affine Subspaces for Robust Object Tracking. IEEE SigPort. http://sigport.org/185
Sareh Shirazi, Conrad Sanderson, Chris McCool, Mehrtash Harandi, 2015. Bags of Affine Subspaces for Robust Object Tracking. Available at: http://sigport.org/185.
Sareh Shirazi, Conrad Sanderson, Chris McCool, Mehrtash Harandi. (2015). "Bags of Affine Subspaces for Robust Object Tracking." Web.
1. Sareh Shirazi, Conrad Sanderson, Chris McCool, Mehrtash Harandi. Bags of Affine Subspaces for Robust Object Tracking [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/185

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