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Image, Video, and Multidimensional Signal Processing

SINGLE DEPTH IMAGE SUPER-RESOLUTION AND DENOISING BASED ON SPARSE GRAPHS VIA STRUCTURE TENSOR


The existing single depth image super-resolution (SR)
methods suppose that the image to be interpolated is noise
free. However, the supposition is invalid in practice because
noise will be inevitably introduced in the depth image acquisition
process. In this paper, we address the problem of image
denoising and SR jointly based on designing sparse graphs
that are useful for describing the geometric structures of data
domains. In our method, we first cluster similar patches in a
noisy depth image and compute an average patch. Different

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Authors:
Xianming Liu,Yongbing Zhang,Qionghai Dai
Submitted On:
11 September 2017 - 9:36pm
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Yihui Feng_icip_2017.pdf

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[1] Xianming Liu,Yongbing Zhang,Qionghai Dai, "SINGLE DEPTH IMAGE SUPER-RESOLUTION AND DENOISING BASED ON SPARSE GRAPHS VIA STRUCTURE TENSOR", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1922. Accessed: Nov. 23, 2017.
@article{1922-17,
url = {http://sigport.org/1922},
author = {Xianming Liu;Yongbing Zhang;Qionghai Dai },
publisher = {IEEE SigPort},
title = {SINGLE DEPTH IMAGE SUPER-RESOLUTION AND DENOISING BASED ON SPARSE GRAPHS VIA STRUCTURE TENSOR},
year = {2017} }
TY - EJOUR
T1 - SINGLE DEPTH IMAGE SUPER-RESOLUTION AND DENOISING BASED ON SPARSE GRAPHS VIA STRUCTURE TENSOR
AU - Xianming Liu;Yongbing Zhang;Qionghai Dai
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1922
ER -
Xianming Liu,Yongbing Zhang,Qionghai Dai. (2017). SINGLE DEPTH IMAGE SUPER-RESOLUTION AND DENOISING BASED ON SPARSE GRAPHS VIA STRUCTURE TENSOR. IEEE SigPort. http://sigport.org/1922
Xianming Liu,Yongbing Zhang,Qionghai Dai, 2017. SINGLE DEPTH IMAGE SUPER-RESOLUTION AND DENOISING BASED ON SPARSE GRAPHS VIA STRUCTURE TENSOR. Available at: http://sigport.org/1922.
Xianming Liu,Yongbing Zhang,Qionghai Dai. (2017). "SINGLE DEPTH IMAGE SUPER-RESOLUTION AND DENOISING BASED ON SPARSE GRAPHS VIA STRUCTURE TENSOR." Web.
1. Xianming Liu,Yongbing Zhang,Qionghai Dai. SINGLE DEPTH IMAGE SUPER-RESOLUTION AND DENOISING BASED ON SPARSE GRAPHS VIA STRUCTURE TENSOR [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1922

CONVOLUTIONAL NEURAL NETWORKS FOR LICENSE PLATE DETECTION IN IMAGES


License plate detection is a challenging task when dealing with open environments and images captured from a certain distance by lowcost cameras. In this paper, we propose an approach for detecting license plates based on a convolutional neural network which models a function that produces a score for each image sub-region, allowing us to estimate the locations of the detected license plates by combining the results obtained from sparse overlapping regions.

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Authors:
Francisco Delmar Kurpiel, Rodrigo Minetto, Bogdan Tomoyuki Nassu
Submitted On:
11 September 2017 - 2:51pm
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2017-09 - ICIP 2017.pdf

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[1] Francisco Delmar Kurpiel, Rodrigo Minetto, Bogdan Tomoyuki Nassu, "CONVOLUTIONAL NEURAL NETWORKS FOR LICENSE PLATE DETECTION IN IMAGES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1915. Accessed: Nov. 23, 2017.
@article{1915-17,
url = {http://sigport.org/1915},
author = {Francisco Delmar Kurpiel; Rodrigo Minetto; Bogdan Tomoyuki Nassu },
publisher = {IEEE SigPort},
title = {CONVOLUTIONAL NEURAL NETWORKS FOR LICENSE PLATE DETECTION IN IMAGES},
year = {2017} }
TY - EJOUR
T1 - CONVOLUTIONAL NEURAL NETWORKS FOR LICENSE PLATE DETECTION IN IMAGES
AU - Francisco Delmar Kurpiel; Rodrigo Minetto; Bogdan Tomoyuki Nassu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1915
ER -
Francisco Delmar Kurpiel, Rodrigo Minetto, Bogdan Tomoyuki Nassu. (2017). CONVOLUTIONAL NEURAL NETWORKS FOR LICENSE PLATE DETECTION IN IMAGES. IEEE SigPort. http://sigport.org/1915
Francisco Delmar Kurpiel, Rodrigo Minetto, Bogdan Tomoyuki Nassu, 2017. CONVOLUTIONAL NEURAL NETWORKS FOR LICENSE PLATE DETECTION IN IMAGES. Available at: http://sigport.org/1915.
Francisco Delmar Kurpiel, Rodrigo Minetto, Bogdan Tomoyuki Nassu. (2017). "CONVOLUTIONAL NEURAL NETWORKS FOR LICENSE PLATE DETECTION IN IMAGES." Web.
1. Francisco Delmar Kurpiel, Rodrigo Minetto, Bogdan Tomoyuki Nassu. CONVOLUTIONAL NEURAL NETWORKS FOR LICENSE PLATE DETECTION IN IMAGES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1915

Investigating the Impact of High Frame Rates on Video Compression

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Authors:
Alex Mackin, Fan Zhang, Miltiadis Alexios Papadopoulos and David Bull
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12 September 2017 - 10:55am
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ICIP-MACKIN.pdf

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[1] Alex Mackin, Fan Zhang, Miltiadis Alexios Papadopoulos and David Bull, "Investigating the Impact of High Frame Rates on Video Compression", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1909. Accessed: Nov. 23, 2017.
@article{1909-17,
url = {http://sigport.org/1909},
author = {Alex Mackin; Fan Zhang; Miltiadis Alexios Papadopoulos and David Bull },
publisher = {IEEE SigPort},
title = {Investigating the Impact of High Frame Rates on Video Compression},
year = {2017} }
TY - EJOUR
T1 - Investigating the Impact of High Frame Rates on Video Compression
AU - Alex Mackin; Fan Zhang; Miltiadis Alexios Papadopoulos and David Bull
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1909
ER -
Alex Mackin, Fan Zhang, Miltiadis Alexios Papadopoulos and David Bull. (2017). Investigating the Impact of High Frame Rates on Video Compression. IEEE SigPort. http://sigport.org/1909
Alex Mackin, Fan Zhang, Miltiadis Alexios Papadopoulos and David Bull, 2017. Investigating the Impact of High Frame Rates on Video Compression. Available at: http://sigport.org/1909.
Alex Mackin, Fan Zhang, Miltiadis Alexios Papadopoulos and David Bull. (2017). "Investigating the Impact of High Frame Rates on Video Compression." Web.
1. Alex Mackin, Fan Zhang, Miltiadis Alexios Papadopoulos and David Bull. Investigating the Impact of High Frame Rates on Video Compression [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1909

Comp-LOP: Complex Form of Local Orientation Plane for Object Tracking


In this paper, we propose complex form of local orientation plane (Comp-LOP) for object tracking. Comp-LOP is a simple but effective descriptor, which is robust to occlusion for object tracking. It effectively considers spatiotemporal relationship between the target and its surrounding regions in a correlation filter framework by the complex form, which successfully deals with the heavy occlusion problem. Moreover, scale estimation is performed to treat target scale variations for improving tracking accuracy.

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12 September 2017 - 10:06am
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ICIP2017_CompLOP_final.pdf

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[1] , "Comp-LOP: Complex Form of Local Orientation Plane for Object Tracking", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1906. Accessed: Nov. 23, 2017.
@article{1906-17,
url = {http://sigport.org/1906},
author = { },
publisher = {IEEE SigPort},
title = {Comp-LOP: Complex Form of Local Orientation Plane for Object Tracking},
year = {2017} }
TY - EJOUR
T1 - Comp-LOP: Complex Form of Local Orientation Plane for Object Tracking
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1906
ER -
. (2017). Comp-LOP: Complex Form of Local Orientation Plane for Object Tracking. IEEE SigPort. http://sigport.org/1906
, 2017. Comp-LOP: Complex Form of Local Orientation Plane for Object Tracking. Available at: http://sigport.org/1906.
. (2017). "Comp-LOP: Complex Form of Local Orientation Plane for Object Tracking." Web.
1. . Comp-LOP: Complex Form of Local Orientation Plane for Object Tracking [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1906

2563 sliding window filter based unknown object pose estimation

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11 September 2017 - 4:13am
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conference_poster_4.pdf

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[1] , "2563 sliding window filter based unknown object pose estimation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1897. Accessed: Nov. 23, 2017.
@article{1897-17,
url = {http://sigport.org/1897},
author = { },
publisher = {IEEE SigPort},
title = {2563 sliding window filter based unknown object pose estimation},
year = {2017} }
TY - EJOUR
T1 - 2563 sliding window filter based unknown object pose estimation
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1897
ER -
. (2017). 2563 sliding window filter based unknown object pose estimation. IEEE SigPort. http://sigport.org/1897
, 2017. 2563 sliding window filter based unknown object pose estimation. Available at: http://sigport.org/1897.
. (2017). "2563 sliding window filter based unknown object pose estimation." Web.
1. . 2563 sliding window filter based unknown object pose estimation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1897

4D Effect Classification by Encoding CNN Features


4D effects are physical effects simulated in sync with videos, movies, and games to augment the events occurring in a story or a virtual world. Types of 4D effects commonly used for the immersive media may include seat motion, vibration, flash, wind, water, scent, thunderstorm, snow, and fog. Currently, the recognition of physical effects from a video is mainly conducted by human experts. Although 4D effects are promising in giving immersive experience and entertainment, this manual production has been the main obstacle to faster and wider application of 4D effects.

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Authors:
Thomhert S. Siadari, Mikyong Han, Hyunjin Yoon
Submitted On:
14 September 2017 - 3:13am
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presentation slides

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[1] Thomhert S. Siadari, Mikyong Han, Hyunjin Yoon, "4D Effect Classification by Encoding CNN Features", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1892. Accessed: Nov. 23, 2017.
@article{1892-17,
url = {http://sigport.org/1892},
author = {Thomhert S. Siadari; Mikyong Han; Hyunjin Yoon },
publisher = {IEEE SigPort},
title = {4D Effect Classification by Encoding CNN Features},
year = {2017} }
TY - EJOUR
T1 - 4D Effect Classification by Encoding CNN Features
AU - Thomhert S. Siadari; Mikyong Han; Hyunjin Yoon
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1892
ER -
Thomhert S. Siadari, Mikyong Han, Hyunjin Yoon. (2017). 4D Effect Classification by Encoding CNN Features. IEEE SigPort. http://sigport.org/1892
Thomhert S. Siadari, Mikyong Han, Hyunjin Yoon, 2017. 4D Effect Classification by Encoding CNN Features. Available at: http://sigport.org/1892.
Thomhert S. Siadari, Mikyong Han, Hyunjin Yoon. (2017). "4D Effect Classification by Encoding CNN Features." Web.
1. Thomhert S. Siadari, Mikyong Han, Hyunjin Yoon. 4D Effect Classification by Encoding CNN Features [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1892

SAR Image Despeckling by Combination of Fractional-Order Total Variation and Nonlocal Low Rank Regularization


This paper proposes a combinational regularization model for synthetic aperture radar (SAR) image despeckling. In contrast to most of the well-known regularization methods that only use one image prior property, the proposed combinational regularization model includes both fractional-order total variation (FrTV) regularization term and nonlocal low rank (NLR) regularization term.

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10 September 2017 - 9:34pm
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ICIP2017-GaoChen.pdf

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[1] , "SAR Image Despeckling by Combination of Fractional-Order Total Variation and Nonlocal Low Rank Regularization ", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1890. Accessed: Nov. 23, 2017.
@article{1890-17,
url = {http://sigport.org/1890},
author = { },
publisher = {IEEE SigPort},
title = {SAR Image Despeckling by Combination of Fractional-Order Total Variation and Nonlocal Low Rank Regularization },
year = {2017} }
TY - EJOUR
T1 - SAR Image Despeckling by Combination of Fractional-Order Total Variation and Nonlocal Low Rank Regularization
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1890
ER -
. (2017). SAR Image Despeckling by Combination of Fractional-Order Total Variation and Nonlocal Low Rank Regularization . IEEE SigPort. http://sigport.org/1890
, 2017. SAR Image Despeckling by Combination of Fractional-Order Total Variation and Nonlocal Low Rank Regularization . Available at: http://sigport.org/1890.
. (2017). "SAR Image Despeckling by Combination of Fractional-Order Total Variation and Nonlocal Low Rank Regularization ." Web.
1. . SAR Image Despeckling by Combination of Fractional-Order Total Variation and Nonlocal Low Rank Regularization [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1890

ByNet-SR: Image Super Resolution with a Bypass Connection Network

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8 September 2017 - 9:13am
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ICIP17-poster.pdf

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[1] , "ByNet-SR: Image Super Resolution with a Bypass Connection Network", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1876. Accessed: Nov. 23, 2017.
@article{1876-17,
url = {http://sigport.org/1876},
author = { },
publisher = {IEEE SigPort},
title = {ByNet-SR: Image Super Resolution with a Bypass Connection Network},
year = {2017} }
TY - EJOUR
T1 - ByNet-SR: Image Super Resolution with a Bypass Connection Network
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1876
ER -
. (2017). ByNet-SR: Image Super Resolution with a Bypass Connection Network. IEEE SigPort. http://sigport.org/1876
, 2017. ByNet-SR: Image Super Resolution with a Bypass Connection Network. Available at: http://sigport.org/1876.
. (2017). "ByNet-SR: Image Super Resolution with a Bypass Connection Network." Web.
1. . ByNet-SR: Image Super Resolution with a Bypass Connection Network [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1876

Inter-Camera Tracking Based On Fully Unsupervised Online Learning


In this paper, we present a novel fully automatic approach to track the same human across multiple disjoint cameras. Our framework includes a two-phase feature extractor and an online-learning-based camera link model estimation. We introduce an effective and robust integration of appearance and context features. Couples are detected automatically, and the couple feature is also integrated with appearance features effectively. The proposed algorithm is scalable with the use of a fully unsupervised online learning framework.

poster_2.pdf

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Authors:
Young-Gun Lee, Zheng Tang, Jenq-Neng Hwang, Zhijun Fang
Submitted On:
8 September 2017 - 3:10am
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poster_2.pdf

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[1] Young-Gun Lee, Zheng Tang, Jenq-Neng Hwang, Zhijun Fang, "Inter-Camera Tracking Based On Fully Unsupervised Online Learning", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1869. Accessed: Nov. 23, 2017.
@article{1869-17,
url = {http://sigport.org/1869},
author = {Young-Gun Lee; Zheng Tang; Jenq-Neng Hwang; Zhijun Fang },
publisher = {IEEE SigPort},
title = {Inter-Camera Tracking Based On Fully Unsupervised Online Learning},
year = {2017} }
TY - EJOUR
T1 - Inter-Camera Tracking Based On Fully Unsupervised Online Learning
AU - Young-Gun Lee; Zheng Tang; Jenq-Neng Hwang; Zhijun Fang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1869
ER -
Young-Gun Lee, Zheng Tang, Jenq-Neng Hwang, Zhijun Fang. (2017). Inter-Camera Tracking Based On Fully Unsupervised Online Learning. IEEE SigPort. http://sigport.org/1869
Young-Gun Lee, Zheng Tang, Jenq-Neng Hwang, Zhijun Fang, 2017. Inter-Camera Tracking Based On Fully Unsupervised Online Learning. Available at: http://sigport.org/1869.
Young-Gun Lee, Zheng Tang, Jenq-Neng Hwang, Zhijun Fang. (2017). "Inter-Camera Tracking Based On Fully Unsupervised Online Learning." Web.
1. Young-Gun Lee, Zheng Tang, Jenq-Neng Hwang, Zhijun Fang. Inter-Camera Tracking Based On Fully Unsupervised Online Learning [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1869

Prostate detection and segmentation based on convolutional neural network and topological derivative


The topological derivative (TD) for shape analysis has been employed
in image segmentation, and machine learning schemes based on
convolutional neural network (CNN) provide the high performance in
the image processing. The supervised and unsupervised approaches
have different roles and advantages according to their concepts. To
maximize the benefits of two approaches, we propose CNN-TD based
segmentation approach. A CNN-based segmentation scheme is employed
to faithfully consider the characteristics of an object to be

Paper Details

Authors:
Young Han Lee, Sangkeun Lee
Submitted On:
3 September 2017 - 9:24pm
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20170918-ICIP-TD-DL segmentation_Cho.pdf

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[1] Young Han Lee, Sangkeun Lee, "Prostate detection and segmentation based on convolutional neural network and topological derivative", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1823. Accessed: Nov. 23, 2017.
@article{1823-17,
url = {http://sigport.org/1823},
author = {Young Han Lee; Sangkeun Lee },
publisher = {IEEE SigPort},
title = {Prostate detection and segmentation based on convolutional neural network and topological derivative},
year = {2017} }
TY - EJOUR
T1 - Prostate detection and segmentation based on convolutional neural network and topological derivative
AU - Young Han Lee; Sangkeun Lee
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1823
ER -
Young Han Lee, Sangkeun Lee. (2017). Prostate detection and segmentation based on convolutional neural network and topological derivative. IEEE SigPort. http://sigport.org/1823
Young Han Lee, Sangkeun Lee, 2017. Prostate detection and segmentation based on convolutional neural network and topological derivative. Available at: http://sigport.org/1823.
Young Han Lee, Sangkeun Lee. (2017). "Prostate detection and segmentation based on convolutional neural network and topological derivative." Web.
1. Young Han Lee, Sangkeun Lee. Prostate detection and segmentation based on convolutional neural network and topological derivative [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1823

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