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Image/Video Processing

A STUDY OF SUBJECTIVE VIDEO QUALITY AT VARIOUS SPATIAL RESOLUTIONS

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Authors:
Alex Mackin, Mariana Afonso, Fan Zhang, David Bull
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4 October 2018 - 11:48am
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[1] Alex Mackin, Mariana Afonso, Fan Zhang, David Bull, "A STUDY OF SUBJECTIVE VIDEO QUALITY AT VARIOUS SPATIAL RESOLUTIONS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3448. Accessed: Mar. 20, 2019.
@article{3448-18,
url = {http://sigport.org/3448},
author = {Alex Mackin; Mariana Afonso; Fan Zhang; David Bull },
publisher = {IEEE SigPort},
title = {A STUDY OF SUBJECTIVE VIDEO QUALITY AT VARIOUS SPATIAL RESOLUTIONS},
year = {2018} }
TY - EJOUR
T1 - A STUDY OF SUBJECTIVE VIDEO QUALITY AT VARIOUS SPATIAL RESOLUTIONS
AU - Alex Mackin; Mariana Afonso; Fan Zhang; David Bull
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3448
ER -
Alex Mackin, Mariana Afonso, Fan Zhang, David Bull. (2018). A STUDY OF SUBJECTIVE VIDEO QUALITY AT VARIOUS SPATIAL RESOLUTIONS. IEEE SigPort. http://sigport.org/3448
Alex Mackin, Mariana Afonso, Fan Zhang, David Bull, 2018. A STUDY OF SUBJECTIVE VIDEO QUALITY AT VARIOUS SPATIAL RESOLUTIONS. Available at: http://sigport.org/3448.
Alex Mackin, Mariana Afonso, Fan Zhang, David Bull. (2018). "A STUDY OF SUBJECTIVE VIDEO QUALITY AT VARIOUS SPATIAL RESOLUTIONS." Web.
1. Alex Mackin, Mariana Afonso, Fan Zhang, David Bull. A STUDY OF SUBJECTIVE VIDEO QUALITY AT VARIOUS SPATIAL RESOLUTIONS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3448

Poster - Topological Eulerian Synthesis of Slow Motion Periodic Videos


We consider the problem of taking a video that is comprised of multiple periods of repetitive motion, and reordering the frames of the video into a single period, producing a detailed, single cycle video of motion. This problem is challenging, as such videos often contain noise, drift due to camera motion and from cycle to cycle, and irrelevant background motion/occlusions, and these factors can confound the relevant periodic motion we seek in the video.

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Authors:
Christopher J. Tralie, Matthew Berger
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4 October 2018 - 11:43am
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[1] Christopher J. Tralie, Matthew Berger, "Poster - Topological Eulerian Synthesis of Slow Motion Periodic Videos", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3444. Accessed: Mar. 20, 2019.
@article{3444-18,
url = {http://sigport.org/3444},
author = {Christopher J. Tralie; Matthew Berger },
publisher = {IEEE SigPort},
title = {Poster - Topological Eulerian Synthesis of Slow Motion Periodic Videos},
year = {2018} }
TY - EJOUR
T1 - Poster - Topological Eulerian Synthesis of Slow Motion Periodic Videos
AU - Christopher J. Tralie; Matthew Berger
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3444
ER -
Christopher J. Tralie, Matthew Berger. (2018). Poster - Topological Eulerian Synthesis of Slow Motion Periodic Videos. IEEE SigPort. http://sigport.org/3444
Christopher J. Tralie, Matthew Berger, 2018. Poster - Topological Eulerian Synthesis of Slow Motion Periodic Videos. Available at: http://sigport.org/3444.
Christopher J. Tralie, Matthew Berger. (2018). "Poster - Topological Eulerian Synthesis of Slow Motion Periodic Videos." Web.
1. Christopher J. Tralie, Matthew Berger. Poster - Topological Eulerian Synthesis of Slow Motion Periodic Videos [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3444

PMHI: Proposals From Motion History Images for Temporal Segmentation of Long Uncut Videos


This letter proposes a method for the generation of temporal action proposals for the segmentation of long uncut video sequences. The presence of consecutive multiple actions in video sequences makes the temporal segmentation a challenging problem due to the unconstrained nature of actions in space and time. To address this issue, we exploit the nonaction segments present between the actual human actions in uncut videos. From the long uncut video, we compute the energy of consecutive nonoverlapping motion history images (MHIs), which provides spatiotemporal information of motion.

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Authors:
Fiza Murtaza, Muhammad Haroon Yousaf, Sergio A Velastin
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4 October 2018 - 11:39am
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[1] Fiza Murtaza, Muhammad Haroon Yousaf, Sergio A Velastin, "PMHI: Proposals From Motion History Images for Temporal Segmentation of Long Uncut Videos", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3441. Accessed: Mar. 20, 2019.
@article{3441-18,
url = {http://sigport.org/3441},
author = {Fiza Murtaza; Muhammad Haroon Yousaf; Sergio A Velastin },
publisher = {IEEE SigPort},
title = {PMHI: Proposals From Motion History Images for Temporal Segmentation of Long Uncut Videos},
year = {2018} }
TY - EJOUR
T1 - PMHI: Proposals From Motion History Images for Temporal Segmentation of Long Uncut Videos
AU - Fiza Murtaza; Muhammad Haroon Yousaf; Sergio A Velastin
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3441
ER -
Fiza Murtaza, Muhammad Haroon Yousaf, Sergio A Velastin. (2018). PMHI: Proposals From Motion History Images for Temporal Segmentation of Long Uncut Videos. IEEE SigPort. http://sigport.org/3441
Fiza Murtaza, Muhammad Haroon Yousaf, Sergio A Velastin, 2018. PMHI: Proposals From Motion History Images for Temporal Segmentation of Long Uncut Videos. Available at: http://sigport.org/3441.
Fiza Murtaza, Muhammad Haroon Yousaf, Sergio A Velastin. (2018). "PMHI: Proposals From Motion History Images for Temporal Segmentation of Long Uncut Videos." Web.
1. Fiza Murtaza, Muhammad Haroon Yousaf, Sergio A Velastin. PMHI: Proposals From Motion History Images for Temporal Segmentation of Long Uncut Videos [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3441

REAL-TIME REGISTRATION OF RGB-D IMAGE PAIR FOR SEE-THROUGH SYSTEM


This paper presents a dense method of real-time registration of RGB-D image pair. So far, we have proposed the “see-
through system”, in which multiple images acquired from RGB-D sensors are integrated to present images that is useful for confirming the shape or the positions of objects behind obstacles. However, errors of positional relation of sensors re-

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Authors:
Tatsuya Kittaka, Hiromitsu Fujii, Atsushi Yamashita, Hajime Asama
Submitted On:
4 October 2018 - 11:37am
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[1] Tatsuya Kittaka, Hiromitsu Fujii, Atsushi Yamashita, Hajime Asama, "REAL-TIME REGISTRATION OF RGB-D IMAGE PAIR FOR SEE-THROUGH SYSTEM", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3440. Accessed: Mar. 20, 2019.
@article{3440-18,
url = {http://sigport.org/3440},
author = {Tatsuya Kittaka; Hiromitsu Fujii; Atsushi Yamashita; Hajime Asama },
publisher = {IEEE SigPort},
title = {REAL-TIME REGISTRATION OF RGB-D IMAGE PAIR FOR SEE-THROUGH SYSTEM},
year = {2018} }
TY - EJOUR
T1 - REAL-TIME REGISTRATION OF RGB-D IMAGE PAIR FOR SEE-THROUGH SYSTEM
AU - Tatsuya Kittaka; Hiromitsu Fujii; Atsushi Yamashita; Hajime Asama
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3440
ER -
Tatsuya Kittaka, Hiromitsu Fujii, Atsushi Yamashita, Hajime Asama. (2018). REAL-TIME REGISTRATION OF RGB-D IMAGE PAIR FOR SEE-THROUGH SYSTEM. IEEE SigPort. http://sigport.org/3440
Tatsuya Kittaka, Hiromitsu Fujii, Atsushi Yamashita, Hajime Asama, 2018. REAL-TIME REGISTRATION OF RGB-D IMAGE PAIR FOR SEE-THROUGH SYSTEM. Available at: http://sigport.org/3440.
Tatsuya Kittaka, Hiromitsu Fujii, Atsushi Yamashita, Hajime Asama. (2018). "REAL-TIME REGISTRATION OF RGB-D IMAGE PAIR FOR SEE-THROUGH SYSTEM." Web.
1. Tatsuya Kittaka, Hiromitsu Fujii, Atsushi Yamashita, Hajime Asama. REAL-TIME REGISTRATION OF RGB-D IMAGE PAIR FOR SEE-THROUGH SYSTEM [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3440

MAXIMUM LIKELIHOOD ESTIMATION OF REGULARISATION PARAMETERS


This paper presents an empirical Bayesian method to estimate regularisation parameters in imaging inverse problems. The method calibrates regularisation parameters directly from the observed data by maximum marginal likelihood estimation, and is useful for inverse problems that are convex. A main novelty is that maximum likelihood estimation is performed efficiently by using a stochastic proximal gradient algorithm that is driven by two proximal Markov chain Monte Carlo samplers, intimately combining modern optimisation and sampling techniques.

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Authors:
Marcelo Pereyra
Submitted On:
4 October 2018 - 12:19pm
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Poster ICIP Vidal 3oct18.pdf

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[1] Marcelo Pereyra, "MAXIMUM LIKELIHOOD ESTIMATION OF REGULARISATION PARAMETERS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3436. Accessed: Mar. 20, 2019.
@article{3436-18,
url = {http://sigport.org/3436},
author = {Marcelo Pereyra },
publisher = {IEEE SigPort},
title = {MAXIMUM LIKELIHOOD ESTIMATION OF REGULARISATION PARAMETERS},
year = {2018} }
TY - EJOUR
T1 - MAXIMUM LIKELIHOOD ESTIMATION OF REGULARISATION PARAMETERS
AU - Marcelo Pereyra
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3436
ER -
Marcelo Pereyra. (2018). MAXIMUM LIKELIHOOD ESTIMATION OF REGULARISATION PARAMETERS. IEEE SigPort. http://sigport.org/3436
Marcelo Pereyra, 2018. MAXIMUM LIKELIHOOD ESTIMATION OF REGULARISATION PARAMETERS. Available at: http://sigport.org/3436.
Marcelo Pereyra. (2018). "MAXIMUM LIKELIHOOD ESTIMATION OF REGULARISATION PARAMETERS." Web.
1. Marcelo Pereyra. MAXIMUM LIKELIHOOD ESTIMATION OF REGULARISATION PARAMETERS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3436

BAYESIAN APPROACH FOR AUTOMATIC JOINT PARAMETER ESTIMATION IN 3D IMAGE RECONSTRUCTION FROM MULTI-FOCUS MICROSCOPE

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Authors:
Seunghwan Yoo, Pablo Ruiz, Xiang Huang, Kuan He, Xiaolei Wang, Itay Gdor, Alan Selewa, Matthew Daddysman, Nicola J. Ferrier, Mark Hereld, Norbert Scherer, Oliver Cossairt, and Aggelos K. Katsaggelos
Submitted On:
4 October 2018 - 11:22am
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[1] Seunghwan Yoo, Pablo Ruiz, Xiang Huang, Kuan He, Xiaolei Wang, Itay Gdor, Alan Selewa, Matthew Daddysman, Nicola J. Ferrier, Mark Hereld, Norbert Scherer, Oliver Cossairt, and Aggelos K. Katsaggelos, "BAYESIAN APPROACH FOR AUTOMATIC JOINT PARAMETER ESTIMATION IN 3D IMAGE RECONSTRUCTION FROM MULTI-FOCUS MICROSCOPE", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3434. Accessed: Mar. 20, 2019.
@article{3434-18,
url = {http://sigport.org/3434},
author = {Seunghwan Yoo; Pablo Ruiz; Xiang Huang; Kuan He; Xiaolei Wang; Itay Gdor; Alan Selewa; Matthew Daddysman; Nicola J. Ferrier; Mark Hereld; Norbert Scherer; Oliver Cossairt; and Aggelos K. Katsaggelos },
publisher = {IEEE SigPort},
title = {BAYESIAN APPROACH FOR AUTOMATIC JOINT PARAMETER ESTIMATION IN 3D IMAGE RECONSTRUCTION FROM MULTI-FOCUS MICROSCOPE},
year = {2018} }
TY - EJOUR
T1 - BAYESIAN APPROACH FOR AUTOMATIC JOINT PARAMETER ESTIMATION IN 3D IMAGE RECONSTRUCTION FROM MULTI-FOCUS MICROSCOPE
AU - Seunghwan Yoo; Pablo Ruiz; Xiang Huang; Kuan He; Xiaolei Wang; Itay Gdor; Alan Selewa; Matthew Daddysman; Nicola J. Ferrier; Mark Hereld; Norbert Scherer; Oliver Cossairt; and Aggelos K. Katsaggelos
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3434
ER -
Seunghwan Yoo, Pablo Ruiz, Xiang Huang, Kuan He, Xiaolei Wang, Itay Gdor, Alan Selewa, Matthew Daddysman, Nicola J. Ferrier, Mark Hereld, Norbert Scherer, Oliver Cossairt, and Aggelos K. Katsaggelos. (2018). BAYESIAN APPROACH FOR AUTOMATIC JOINT PARAMETER ESTIMATION IN 3D IMAGE RECONSTRUCTION FROM MULTI-FOCUS MICROSCOPE. IEEE SigPort. http://sigport.org/3434
Seunghwan Yoo, Pablo Ruiz, Xiang Huang, Kuan He, Xiaolei Wang, Itay Gdor, Alan Selewa, Matthew Daddysman, Nicola J. Ferrier, Mark Hereld, Norbert Scherer, Oliver Cossairt, and Aggelos K. Katsaggelos, 2018. BAYESIAN APPROACH FOR AUTOMATIC JOINT PARAMETER ESTIMATION IN 3D IMAGE RECONSTRUCTION FROM MULTI-FOCUS MICROSCOPE. Available at: http://sigport.org/3434.
Seunghwan Yoo, Pablo Ruiz, Xiang Huang, Kuan He, Xiaolei Wang, Itay Gdor, Alan Selewa, Matthew Daddysman, Nicola J. Ferrier, Mark Hereld, Norbert Scherer, Oliver Cossairt, and Aggelos K. Katsaggelos. (2018). "BAYESIAN APPROACH FOR AUTOMATIC JOINT PARAMETER ESTIMATION IN 3D IMAGE RECONSTRUCTION FROM MULTI-FOCUS MICROSCOPE." Web.
1. Seunghwan Yoo, Pablo Ruiz, Xiang Huang, Kuan He, Xiaolei Wang, Itay Gdor, Alan Selewa, Matthew Daddysman, Nicola J. Ferrier, Mark Hereld, Norbert Scherer, Oliver Cossairt, and Aggelos K. Katsaggelos. BAYESIAN APPROACH FOR AUTOMATIC JOINT PARAMETER ESTIMATION IN 3D IMAGE RECONSTRUCTION FROM MULTI-FOCUS MICROSCOPE [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3434

ICIP18001


Traditional methods for multiple object tracking usually consider features at image level and reason about simple space and time constraints. However, in this paper we propose a multiple object tracker based on LSTM network to learn temporally correlated features. Our tracker learns features on velocity, position and appearance aspects of the objects to improve tracking accuracy. In order to deal with occlusion problems, velocity model is also used to predict the lost detections for the occluded identities.

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Authors:
Yiming Liang, Yue Zhou
Submitted On:
4 October 2018 - 11:02am
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ICIP18001

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[1] Yiming Liang, Yue Zhou, "ICIP18001", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3429. Accessed: Mar. 20, 2019.
@article{3429-18,
url = {http://sigport.org/3429},
author = {Yiming Liang; Yue Zhou },
publisher = {IEEE SigPort},
title = {ICIP18001},
year = {2018} }
TY - EJOUR
T1 - ICIP18001
AU - Yiming Liang; Yue Zhou
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3429
ER -
Yiming Liang, Yue Zhou. (2018). ICIP18001. IEEE SigPort. http://sigport.org/3429
Yiming Liang, Yue Zhou, 2018. ICIP18001. Available at: http://sigport.org/3429.
Yiming Liang, Yue Zhou. (2018). "ICIP18001." Web.
1. Yiming Liang, Yue Zhou. ICIP18001 [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3429

SPECTRAL GRAPH WAVELET BASED NONRIGID IMAGE REGISTRATION


We propose a nonrigid registration method whose motion estimation is cast into a feature matching problem under the Log-Demons framework using Graph Wavelets. We investigate the Spectral Graph Wavelets (SGWs) to capture the shape features of the images. The SGWs are more adapted to learn the spatial and geometric organization of data with complex structures than the classical wavelets. Our experiments on T1 brain images and endomicroscopic images show that this method outperforms the existing nonrigid image registration techniques (i.e.

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Authors:
Nhung Pham, David Helbert, Pascal Bourdon, Philippe Carré
Submitted On:
4 October 2018 - 10:13am
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Nhung_ICIP2018_landscape.pdf

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[1] Nhung Pham, David Helbert, Pascal Bourdon, Philippe Carré, "SPECTRAL GRAPH WAVELET BASED NONRIGID IMAGE REGISTRATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3419. Accessed: Mar. 20, 2019.
@article{3419-18,
url = {http://sigport.org/3419},
author = {Nhung Pham; David Helbert; Pascal Bourdon; Philippe Carré },
publisher = {IEEE SigPort},
title = {SPECTRAL GRAPH WAVELET BASED NONRIGID IMAGE REGISTRATION},
year = {2018} }
TY - EJOUR
T1 - SPECTRAL GRAPH WAVELET BASED NONRIGID IMAGE REGISTRATION
AU - Nhung Pham; David Helbert; Pascal Bourdon; Philippe Carré
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3419
ER -
Nhung Pham, David Helbert, Pascal Bourdon, Philippe Carré. (2018). SPECTRAL GRAPH WAVELET BASED NONRIGID IMAGE REGISTRATION. IEEE SigPort. http://sigport.org/3419
Nhung Pham, David Helbert, Pascal Bourdon, Philippe Carré, 2018. SPECTRAL GRAPH WAVELET BASED NONRIGID IMAGE REGISTRATION. Available at: http://sigport.org/3419.
Nhung Pham, David Helbert, Pascal Bourdon, Philippe Carré. (2018). "SPECTRAL GRAPH WAVELET BASED NONRIGID IMAGE REGISTRATION." Web.
1. Nhung Pham, David Helbert, Pascal Bourdon, Philippe Carré. SPECTRAL GRAPH WAVELET BASED NONRIGID IMAGE REGISTRATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3419

Registration and Fusion of Multi-Spectral Images Using a Novel Edge Descriptor


In this work we propose a fully end-to-end approach for
multi-spectral image registration and fusion. Our fusion
method combines images from different spectral channels
into a single fused image using approaches for low and high
frequency signals. A prerequisite of fusion is the geometric
alignment between the spectral bands, commonly referred to
as registration. Unfortunately, common methods for image
registration of a single spectral channel might prove inaccurate
on images from different modalities. For that end, we

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Submitted On:
4 October 2018 - 9:57am
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[1] , "Registration and Fusion of Multi-Spectral Images Using a Novel Edge Descriptor", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3417. Accessed: Mar. 20, 2019.
@article{3417-18,
url = {http://sigport.org/3417},
author = { },
publisher = {IEEE SigPort},
title = {Registration and Fusion of Multi-Spectral Images Using a Novel Edge Descriptor},
year = {2018} }
TY - EJOUR
T1 - Registration and Fusion of Multi-Spectral Images Using a Novel Edge Descriptor
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3417
ER -
. (2018). Registration and Fusion of Multi-Spectral Images Using a Novel Edge Descriptor. IEEE SigPort. http://sigport.org/3417
, 2018. Registration and Fusion of Multi-Spectral Images Using a Novel Edge Descriptor. Available at: http://sigport.org/3417.
. (2018). "Registration and Fusion of Multi-Spectral Images Using a Novel Edge Descriptor." Web.
1. . Registration and Fusion of Multi-Spectral Images Using a Novel Edge Descriptor [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3417

Deep Multi-Spectral Registration Using Invariant Descriptor Learning


In this work, we propose a deep-learning approach for aligning
cross-spectral images. Our approach utilizes a learned
descriptor invariant to different spectra. Multi-modal images
of the same scene capture different characteristics and therefore
their registration is challenging. To that end, we developed
a feature-based approach for registering visible (VIS)
to Near-Infra-Red (NIR) images. Our scheme detects corners
by Harris and matches them by a patch-metric learned
on top of a network trained using the CIFAR-10 dataset. As

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Submitted On:
4 October 2018 - 9:54am
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[1] , "Deep Multi-Spectral Registration Using Invariant Descriptor Learning", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3416. Accessed: Mar. 20, 2019.
@article{3416-18,
url = {http://sigport.org/3416},
author = { },
publisher = {IEEE SigPort},
title = {Deep Multi-Spectral Registration Using Invariant Descriptor Learning},
year = {2018} }
TY - EJOUR
T1 - Deep Multi-Spectral Registration Using Invariant Descriptor Learning
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3416
ER -
. (2018). Deep Multi-Spectral Registration Using Invariant Descriptor Learning. IEEE SigPort. http://sigport.org/3416
, 2018. Deep Multi-Spectral Registration Using Invariant Descriptor Learning. Available at: http://sigport.org/3416.
. (2018). "Deep Multi-Spectral Registration Using Invariant Descriptor Learning." Web.
1. . Deep Multi-Spectral Registration Using Invariant Descriptor Learning [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3416

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