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Image, Video, and Multidimensional Signal Processing

Immersive Optical-See-Through Augmented Reality (Keynote Talk)


Immersive Optical-See-Through Augmented Reality. Augmented Reality has been getting ready for the last 20 years, and is finally becoming real, powered by progress in enabling technologies such as graphics, vision, sensors, and displays. In this talk I’ll provide a personal retrospective on my journey, working on all those enablers, getting ready for the coming AR revolution. At Meta, we are working on immersive optical-see-through AR headset, as well as the full software stack. We’ll discuss the differences of optical vs.

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Authors:
Kari Pulli
Submitted On:
22 December 2017 - 1:30pm
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ICIP_2017_Meta_AR_small.pdf

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[1] Kari Pulli, "Immersive Optical-See-Through Augmented Reality (Keynote Talk)", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2261. Accessed: Mar. 23, 2019.
@article{2261-17,
url = {http://sigport.org/2261},
author = {Kari Pulli },
publisher = {IEEE SigPort},
title = {Immersive Optical-See-Through Augmented Reality (Keynote Talk)},
year = {2017} }
TY - EJOUR
T1 - Immersive Optical-See-Through Augmented Reality (Keynote Talk)
AU - Kari Pulli
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2261
ER -
Kari Pulli. (2017). Immersive Optical-See-Through Augmented Reality (Keynote Talk). IEEE SigPort. http://sigport.org/2261
Kari Pulli, 2017. Immersive Optical-See-Through Augmented Reality (Keynote Talk). Available at: http://sigport.org/2261.
Kari Pulli. (2017). "Immersive Optical-See-Through Augmented Reality (Keynote Talk)." Web.
1. Kari Pulli. Immersive Optical-See-Through Augmented Reality (Keynote Talk) [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2261

Classification of Severely Occluded Image Sequences via Convolutional Recurrent Neural Networks

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Authors:
Jian Zheng, Yifan Wang, Xiaonan Zhang, Xiaohua Li
Submitted On:
29 November 2018 - 3:44am
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GlobalSIP_poster_Final.pdf

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[1] Jian Zheng, Yifan Wang, Xiaonan Zhang, Xiaohua Li, "Classification of Severely Occluded Image Sequences via Convolutional Recurrent Neural Networks", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3830. Accessed: Mar. 23, 2019.
@article{3830-18,
url = {http://sigport.org/3830},
author = {Jian Zheng; Yifan Wang; Xiaonan Zhang; Xiaohua Li },
publisher = {IEEE SigPort},
title = {Classification of Severely Occluded Image Sequences via Convolutional Recurrent Neural Networks},
year = {2018} }
TY - EJOUR
T1 - Classification of Severely Occluded Image Sequences via Convolutional Recurrent Neural Networks
AU - Jian Zheng; Yifan Wang; Xiaonan Zhang; Xiaohua Li
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3830
ER -
Jian Zheng, Yifan Wang, Xiaonan Zhang, Xiaohua Li. (2018). Classification of Severely Occluded Image Sequences via Convolutional Recurrent Neural Networks. IEEE SigPort. http://sigport.org/3830
Jian Zheng, Yifan Wang, Xiaonan Zhang, Xiaohua Li, 2018. Classification of Severely Occluded Image Sequences via Convolutional Recurrent Neural Networks. Available at: http://sigport.org/3830.
Jian Zheng, Yifan Wang, Xiaonan Zhang, Xiaohua Li. (2018). "Classification of Severely Occluded Image Sequences via Convolutional Recurrent Neural Networks." Web.
1. Jian Zheng, Yifan Wang, Xiaonan Zhang, Xiaohua Li. Classification of Severely Occluded Image Sequences via Convolutional Recurrent Neural Networks [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3830

Sparse tensor recovery via N-mode FISTA with support augmentation

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Authors:
Ashley Prater-Bennette, Lixin Shen
Submitted On:
28 November 2018 - 6:12pm
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PraterBennette_GlobalSIP_1176_Presentation_v3.pdf

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[1] Ashley Prater-Bennette, Lixin Shen, "Sparse tensor recovery via N-mode FISTA with support augmentation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3827. Accessed: Mar. 23, 2019.
@article{3827-18,
url = {http://sigport.org/3827},
author = {Ashley Prater-Bennette; Lixin Shen },
publisher = {IEEE SigPort},
title = {Sparse tensor recovery via N-mode FISTA with support augmentation},
year = {2018} }
TY - EJOUR
T1 - Sparse tensor recovery via N-mode FISTA with support augmentation
AU - Ashley Prater-Bennette; Lixin Shen
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3827
ER -
Ashley Prater-Bennette, Lixin Shen. (2018). Sparse tensor recovery via N-mode FISTA with support augmentation. IEEE SigPort. http://sigport.org/3827
Ashley Prater-Bennette, Lixin Shen, 2018. Sparse tensor recovery via N-mode FISTA with support augmentation. Available at: http://sigport.org/3827.
Ashley Prater-Bennette, Lixin Shen. (2018). "Sparse tensor recovery via N-mode FISTA with support augmentation." Web.
1. Ashley Prater-Bennette, Lixin Shen. Sparse tensor recovery via N-mode FISTA with support augmentation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3827

Sparse tensor recovery via N-mode FISTA with support augmentation

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Authors:
Ashley Prater-Bennette, Lixin Shen
Submitted On:
28 November 2018 - 6:12pm
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PraterBennette_GlobalSIP_1176_Presentation_v3.pdf

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[1] Ashley Prater-Bennette, Lixin Shen, "Sparse tensor recovery via N-mode FISTA with support augmentation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3826. Accessed: Mar. 23, 2019.
@article{3826-18,
url = {http://sigport.org/3826},
author = {Ashley Prater-Bennette; Lixin Shen },
publisher = {IEEE SigPort},
title = {Sparse tensor recovery via N-mode FISTA with support augmentation},
year = {2018} }
TY - EJOUR
T1 - Sparse tensor recovery via N-mode FISTA with support augmentation
AU - Ashley Prater-Bennette; Lixin Shen
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3826
ER -
Ashley Prater-Bennette, Lixin Shen. (2018). Sparse tensor recovery via N-mode FISTA with support augmentation. IEEE SigPort. http://sigport.org/3826
Ashley Prater-Bennette, Lixin Shen, 2018. Sparse tensor recovery via N-mode FISTA with support augmentation. Available at: http://sigport.org/3826.
Ashley Prater-Bennette, Lixin Shen. (2018). "Sparse tensor recovery via N-mode FISTA with support augmentation." Web.
1. Ashley Prater-Bennette, Lixin Shen. Sparse tensor recovery via N-mode FISTA with support augmentation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3826

The Greedy Dirichlet Process Filter - An Online Clustering Multi-Target Tracker


Reliable collision avoidance is one of the main requirements for autonomous driving.
Hence, it is important to correctly estimate the states of an unknown number of static and dynamic objects in real-time.
Here, data association is a major challenge for every multi-target tracker.
We propose a novel multi-target tracker called Greedy Dirichlet Process Filter (GDPF) based on the non-parametric Bayesian model called Dirichlet Processes and the fast posterior computation algorithm Sequential Updating and Greedy Search (SUGS).

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Authors:
Patrick Burger, Hans-Joachim Wuensche
Submitted On:
27 November 2018 - 1:23pm
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gdpf_presentation.zip

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[1] Patrick Burger, Hans-Joachim Wuensche, "The Greedy Dirichlet Process Filter - An Online Clustering Multi-Target Tracker", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3815. Accessed: Mar. 23, 2019.
@article{3815-18,
url = {http://sigport.org/3815},
author = {Patrick Burger; Hans-Joachim Wuensche },
publisher = {IEEE SigPort},
title = {The Greedy Dirichlet Process Filter - An Online Clustering Multi-Target Tracker},
year = {2018} }
TY - EJOUR
T1 - The Greedy Dirichlet Process Filter - An Online Clustering Multi-Target Tracker
AU - Patrick Burger; Hans-Joachim Wuensche
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3815
ER -
Patrick Burger, Hans-Joachim Wuensche. (2018). The Greedy Dirichlet Process Filter - An Online Clustering Multi-Target Tracker. IEEE SigPort. http://sigport.org/3815
Patrick Burger, Hans-Joachim Wuensche, 2018. The Greedy Dirichlet Process Filter - An Online Clustering Multi-Target Tracker. Available at: http://sigport.org/3815.
Patrick Burger, Hans-Joachim Wuensche. (2018). "The Greedy Dirichlet Process Filter - An Online Clustering Multi-Target Tracker." Web.
1. Patrick Burger, Hans-Joachim Wuensche. The Greedy Dirichlet Process Filter - An Online Clustering Multi-Target Tracker [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3815

Interactive Object Segmentation with Noisy Binary Inputs

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Authors:
Gregory Canal, Sivabalan Manivasagam, Shaoheng Liang, Christopher Rozell
Submitted On:
26 November 2018 - 12:51pm
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Canal_globalSIP_poster.pdf

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[1] Gregory Canal, Sivabalan Manivasagam, Shaoheng Liang, Christopher Rozell, "Interactive Object Segmentation with Noisy Binary Inputs", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3777. Accessed: Mar. 23, 2019.
@article{3777-18,
url = {http://sigport.org/3777},
author = {Gregory Canal; Sivabalan Manivasagam; Shaoheng Liang; Christopher Rozell },
publisher = {IEEE SigPort},
title = {Interactive Object Segmentation with Noisy Binary Inputs},
year = {2018} }
TY - EJOUR
T1 - Interactive Object Segmentation with Noisy Binary Inputs
AU - Gregory Canal; Sivabalan Manivasagam; Shaoheng Liang; Christopher Rozell
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3777
ER -
Gregory Canal, Sivabalan Manivasagam, Shaoheng Liang, Christopher Rozell. (2018). Interactive Object Segmentation with Noisy Binary Inputs. IEEE SigPort. http://sigport.org/3777
Gregory Canal, Sivabalan Manivasagam, Shaoheng Liang, Christopher Rozell, 2018. Interactive Object Segmentation with Noisy Binary Inputs. Available at: http://sigport.org/3777.
Gregory Canal, Sivabalan Manivasagam, Shaoheng Liang, Christopher Rozell. (2018). "Interactive Object Segmentation with Noisy Binary Inputs." Web.
1. Gregory Canal, Sivabalan Manivasagam, Shaoheng Liang, Christopher Rozell. Interactive Object Segmentation with Noisy Binary Inputs [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3777

PERFORMANCE EVALUATION OF OBJECTIVE QUALITY METRICS ON HLG-BASED HDR IMAGE CODING


We evaluate the performance of objective quality metrics for high dynamic range (HDR) image coding that uses the transfer function (TF) of the Hybrid Log-Gamma (HLG) method. Previous evaluations of objective metrics for HDR image coding have studied which of them are reliable predictors of perceived quality; however, in those tests, all the non-linear transforms used both for encoding and by the best-performing metrics are essentially very similar and based on visual perception data of detection thresholds for lightness variations.

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Authors:
Marcelo Bertalmío
Submitted On:
29 November 2018 - 9:25pm
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20181129_GlobalSIP_Yasuko.pdf

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[1] Marcelo Bertalmío, "PERFORMANCE EVALUATION OF OBJECTIVE QUALITY METRICS ON HLG-BASED HDR IMAGE CODING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3767. Accessed: Mar. 23, 2019.
@article{3767-18,
url = {http://sigport.org/3767},
author = {Marcelo Bertalmío },
publisher = {IEEE SigPort},
title = {PERFORMANCE EVALUATION OF OBJECTIVE QUALITY METRICS ON HLG-BASED HDR IMAGE CODING},
year = {2018} }
TY - EJOUR
T1 - PERFORMANCE EVALUATION OF OBJECTIVE QUALITY METRICS ON HLG-BASED HDR IMAGE CODING
AU - Marcelo Bertalmío
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3767
ER -
Marcelo Bertalmío. (2018). PERFORMANCE EVALUATION OF OBJECTIVE QUALITY METRICS ON HLG-BASED HDR IMAGE CODING. IEEE SigPort. http://sigport.org/3767
Marcelo Bertalmío, 2018. PERFORMANCE EVALUATION OF OBJECTIVE QUALITY METRICS ON HLG-BASED HDR IMAGE CODING. Available at: http://sigport.org/3767.
Marcelo Bertalmío. (2018). "PERFORMANCE EVALUATION OF OBJECTIVE QUALITY METRICS ON HLG-BASED HDR IMAGE CODING." Web.
1. Marcelo Bertalmío. PERFORMANCE EVALUATION OF OBJECTIVE QUALITY METRICS ON HLG-BASED HDR IMAGE CODING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3767

Single image super-resolution with limited number of filters


In this paper, we propose a single image super-resolution with limited number of filters based on RAISR. RAISR is well known as rapid and accurate super-resolution method which utilizes 864 filters for upscaling. This super-resolution idea utilizes the filter learned with sufficient training set. To get low cost of calculation and comparable image quality with other highly accurate super-resolution methods, the patch of input image is classified into classes by simple hash calculation.

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Submitted On:
22 November 2018 - 11:51pm
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Single image super-resolution with limited number of filters.pptx

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[1] , "Single image super-resolution with limited number of filters", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3733. Accessed: Mar. 23, 2019.
@article{3733-18,
url = {http://sigport.org/3733},
author = { },
publisher = {IEEE SigPort},
title = {Single image super-resolution with limited number of filters},
year = {2018} }
TY - EJOUR
T1 - Single image super-resolution with limited number of filters
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3733
ER -
. (2018). Single image super-resolution with limited number of filters. IEEE SigPort. http://sigport.org/3733
, 2018. Single image super-resolution with limited number of filters. Available at: http://sigport.org/3733.
. (2018). "Single image super-resolution with limited number of filters." Web.
1. . Single image super-resolution with limited number of filters [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3733

LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR


Classification subnetwork and box regression subnetwork are
essential components in deep networks for object detection.
However, we observe a contradiction that before NMS, some
better localized detections do not correspond to higher classification confidences, and vice versa. This contradiction exists because classification confidences can not fully reflect the
localization-quality (loc-quality) of each detection. In this
work, we propose the Localization-quality Estimation embedded Detector abbreviated as LED, and a corresponding

Paper Details

Authors:
Shiquan Zhang,Xu Zhao,Liangji Fang,Haiping Fei,Haitao Song
Submitted On:
9 October 2018 - 6:52am
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ICIP_oral_ShiquanZhang_LED

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[1] Shiquan Zhang,Xu Zhao,Liangji Fang,Haiping Fei,Haitao Song, "LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3655. Accessed: Mar. 23, 2019.
@article{3655-18,
url = {http://sigport.org/3655},
author = {Shiquan Zhang;Xu Zhao;Liangji Fang;Haiping Fei;Haitao Song },
publisher = {IEEE SigPort},
title = {LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR},
year = {2018} }
TY - EJOUR
T1 - LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR
AU - Shiquan Zhang;Xu Zhao;Liangji Fang;Haiping Fei;Haitao Song
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3655
ER -
Shiquan Zhang,Xu Zhao,Liangji Fang,Haiping Fei,Haitao Song. (2018). LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE SigPort. http://sigport.org/3655
Shiquan Zhang,Xu Zhao,Liangji Fang,Haiping Fei,Haitao Song, 2018. LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. Available at: http://sigport.org/3655.
Shiquan Zhang,Xu Zhao,Liangji Fang,Haiping Fei,Haitao Song. (2018). "LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR." Web.
1. Shiquan Zhang,Xu Zhao,Liangji Fang,Haiping Fei,Haitao Song. LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3655

Image Fusion and Reconstruction of Compressed Data: A Joint Approach


In the context of data fusion, pansharpening refers to the combination of a panchromatic (PAN) and a multispectral (MS) image, aimed at generating an image that features both the high spatial resolution of the former and high spectral diversity of the latter.
In this work we present a model to jointly solve the problem of data fusion and reconstruction of a compressed image; the latter is envisioned to be generated solely with optical on-board instruments, and stored in place of the original sources.

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Authors:
Laurent Condat, Florian Cotte, Mauro Dalla Mura
Submitted On:
8 October 2018 - 7:37pm
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Presentation_ICIP2018_v3.pdf

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[1] Laurent Condat, Florian Cotte, Mauro Dalla Mura, "Image Fusion and Reconstruction of Compressed Data: A Joint Approach", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3651. Accessed: Mar. 23, 2019.
@article{3651-18,
url = {http://sigport.org/3651},
author = {Laurent Condat; Florian Cotte; Mauro Dalla Mura },
publisher = {IEEE SigPort},
title = {Image Fusion and Reconstruction of Compressed Data: A Joint Approach},
year = {2018} }
TY - EJOUR
T1 - Image Fusion and Reconstruction of Compressed Data: A Joint Approach
AU - Laurent Condat; Florian Cotte; Mauro Dalla Mura
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3651
ER -
Laurent Condat, Florian Cotte, Mauro Dalla Mura. (2018). Image Fusion and Reconstruction of Compressed Data: A Joint Approach. IEEE SigPort. http://sigport.org/3651
Laurent Condat, Florian Cotte, Mauro Dalla Mura, 2018. Image Fusion and Reconstruction of Compressed Data: A Joint Approach. Available at: http://sigport.org/3651.
Laurent Condat, Florian Cotte, Mauro Dalla Mura. (2018). "Image Fusion and Reconstruction of Compressed Data: A Joint Approach." Web.
1. Laurent Condat, Florian Cotte, Mauro Dalla Mura. Image Fusion and Reconstruction of Compressed Data: A Joint Approach [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3651

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