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ICIP 2018

The International Conference on Image Processing (ICIP), sponsored by the IEEE Signal Processing Society, is the premier forum for the presentation of technological advances and research results in the fields of theoretical, experimental, and applied image and video processing. ICIP has been held annually since 1994, brings together leading engineers and scientists in image and video processing from around the world. Visit website.

Distortion-Robust Spherical Camera Motion Estimation via Dense Optical Flow


Conventional techniques for frame-to-frame camera motion estimation rely on tracking a set of sparse feature points. However, images taken from spherical cameras have high distortion which can induce mistakes in feature point tracking, offsetting the advantage of their large fields-of-view. Hence, in this research, we attempt a novel approach of using dense optical flow for distortion-robust spherical camera motion estimation. Dense optical flow incorporates smoothing terms and is free of local outliers. It encodes the camera motion as well as dense 3D information.

Paper Details

Authors:
Alessandro Moro, Hiromitsu Fujii, Atsushi Yamashita, Hajime Asama
Submitted On:
18 October 2018 - 2:08am
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Conference Poster

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[1] Alessandro Moro, Hiromitsu Fujii, Atsushi Yamashita, Hajime Asama, "Distortion-Robust Spherical Camera Motion Estimation via Dense Optical Flow", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3667. Accessed: Oct. 19, 2018.
@article{3667-18,
url = {http://sigport.org/3667},
author = {Alessandro Moro; Hiromitsu Fujii; Atsushi Yamashita; Hajime Asama },
publisher = {IEEE SigPort},
title = {Distortion-Robust Spherical Camera Motion Estimation via Dense Optical Flow},
year = {2018} }
TY - EJOUR
T1 - Distortion-Robust Spherical Camera Motion Estimation via Dense Optical Flow
AU - Alessandro Moro; Hiromitsu Fujii; Atsushi Yamashita; Hajime Asama
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3667
ER -
Alessandro Moro, Hiromitsu Fujii, Atsushi Yamashita, Hajime Asama. (2018). Distortion-Robust Spherical Camera Motion Estimation via Dense Optical Flow. IEEE SigPort. http://sigport.org/3667
Alessandro Moro, Hiromitsu Fujii, Atsushi Yamashita, Hajime Asama, 2018. Distortion-Robust Spherical Camera Motion Estimation via Dense Optical Flow. Available at: http://sigport.org/3667.
Alessandro Moro, Hiromitsu Fujii, Atsushi Yamashita, Hajime Asama. (2018). "Distortion-Robust Spherical Camera Motion Estimation via Dense Optical Flow." Web.
1. Alessandro Moro, Hiromitsu Fujii, Atsushi Yamashita, Hajime Asama. Distortion-Robust Spherical Camera Motion Estimation via Dense Optical Flow [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3667

Recurrent and Dynamic Models for Predicting Streaming Video Quality of Experience

Paper Details

Authors:
Christos Bampis, Zhi Li, Ioannis Katsavounidis, Alan C. Bovik
Submitted On:
14 October 2018 - 10:55am
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GNARX_ICIP_2018_Poster.pdf

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[1] Christos Bampis, Zhi Li, Ioannis Katsavounidis, Alan C. Bovik, "Recurrent and Dynamic Models for Predicting Streaming Video Quality of Experience", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3664. Accessed: Oct. 19, 2018.
@article{3664-18,
url = {http://sigport.org/3664},
author = {Christos Bampis; Zhi Li; Ioannis Katsavounidis; Alan C. Bovik },
publisher = {IEEE SigPort},
title = {Recurrent and Dynamic Models for Predicting Streaming Video Quality of Experience},
year = {2018} }
TY - EJOUR
T1 - Recurrent and Dynamic Models for Predicting Streaming Video Quality of Experience
AU - Christos Bampis; Zhi Li; Ioannis Katsavounidis; Alan C. Bovik
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3664
ER -
Christos Bampis, Zhi Li, Ioannis Katsavounidis, Alan C. Bovik. (2018). Recurrent and Dynamic Models for Predicting Streaming Video Quality of Experience. IEEE SigPort. http://sigport.org/3664
Christos Bampis, Zhi Li, Ioannis Katsavounidis, Alan C. Bovik, 2018. Recurrent and Dynamic Models for Predicting Streaming Video Quality of Experience. Available at: http://sigport.org/3664.
Christos Bampis, Zhi Li, Ioannis Katsavounidis, Alan C. Bovik. (2018). "Recurrent and Dynamic Models for Predicting Streaming Video Quality of Experience." Web.
1. Christos Bampis, Zhi Li, Ioannis Katsavounidis, Alan C. Bovik. Recurrent and Dynamic Models for Predicting Streaming Video Quality of Experience [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3664

Profile Hidden Markov Models for Foreground Object Modelling


Accurate background/foreground segmentation is a preliminary process essential to most visual surveillance applications. With the increasing use of freely moving cameras, strategies have been proposed to refine initial segmentation. In this paper, it is proposed to exploit the Vide-omics paradigm, and Profile Hidden Markov Models in particular, to create a new type of object descriptors relying on spatiotemporal information. Performance of the proposed methodology has been evaluated using a standard dataset of videos captured by moving cameras.

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Authors:
Francisco Florez-Revuelta, Jean-Christophe Nebel
Submitted On:
12 October 2018 - 7:02am
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Bioinformatics-inspired video analysis

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[1] Francisco Florez-Revuelta, Jean-Christophe Nebel, "Profile Hidden Markov Models for Foreground Object Modelling", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3663. Accessed: Oct. 19, 2018.
@article{3663-18,
url = {http://sigport.org/3663},
author = {Francisco Florez-Revuelta; Jean-Christophe Nebel },
publisher = {IEEE SigPort},
title = {Profile Hidden Markov Models for Foreground Object Modelling},
year = {2018} }
TY - EJOUR
T1 - Profile Hidden Markov Models for Foreground Object Modelling
AU - Francisco Florez-Revuelta; Jean-Christophe Nebel
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3663
ER -
Francisco Florez-Revuelta, Jean-Christophe Nebel. (2018). Profile Hidden Markov Models for Foreground Object Modelling. IEEE SigPort. http://sigport.org/3663
Francisco Florez-Revuelta, Jean-Christophe Nebel, 2018. Profile Hidden Markov Models for Foreground Object Modelling. Available at: http://sigport.org/3663.
Francisco Florez-Revuelta, Jean-Christophe Nebel. (2018). "Profile Hidden Markov Models for Foreground Object Modelling." Web.
1. Francisco Florez-Revuelta, Jean-Christophe Nebel. Profile Hidden Markov Models for Foreground Object Modelling [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3663

Code: LOW-FREQUENCY IMAGE NOISE REMOVAL USING WHITE NOISE FILTER


Image noise filters usually assume noise as white Gaussian. However, in a capturing pipeline, noise often becomes spatially correlated due to in-camera processing that aims to suppress the noise and increase the compression rate. Mostly, only high-frequency noise components are suppressed since the image signal is more likely to appear in the low-frequency components of the captured image. As a result, noise emerges as coarse grain which makes white (all-pass) noise filters ineffective, especially when the resolution of the target display is lower than the captured image.

Paper Details

Authors:
Meisam Rakhshanfar and Maria A. Amer
Submitted On:
10 October 2018 - 6:38pm
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Source code; see also https://users.encs.concordia.ca/~amer/LFNFilter/

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[1] Meisam Rakhshanfar and Maria A. Amer, "Code: LOW-FREQUENCY IMAGE NOISE REMOVAL USING WHITE NOISE FILTER", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3659. Accessed: Oct. 19, 2018.
@article{3659-18,
url = {http://sigport.org/3659},
author = {Meisam Rakhshanfar and Maria A. Amer },
publisher = {IEEE SigPort},
title = {Code: LOW-FREQUENCY IMAGE NOISE REMOVAL USING WHITE NOISE FILTER},
year = {2018} }
TY - EJOUR
T1 - Code: LOW-FREQUENCY IMAGE NOISE REMOVAL USING WHITE NOISE FILTER
AU - Meisam Rakhshanfar and Maria A. Amer
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3659
ER -
Meisam Rakhshanfar and Maria A. Amer. (2018). Code: LOW-FREQUENCY IMAGE NOISE REMOVAL USING WHITE NOISE FILTER. IEEE SigPort. http://sigport.org/3659
Meisam Rakhshanfar and Maria A. Amer, 2018. Code: LOW-FREQUENCY IMAGE NOISE REMOVAL USING WHITE NOISE FILTER. Available at: http://sigport.org/3659.
Meisam Rakhshanfar and Maria A. Amer. (2018). "Code: LOW-FREQUENCY IMAGE NOISE REMOVAL USING WHITE NOISE FILTER." Web.
1. Meisam Rakhshanfar and Maria A. Amer. Code: LOW-FREQUENCY IMAGE NOISE REMOVAL USING WHITE NOISE FILTER [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3659

SEGMENTATION OF LUNG TUMOR IN CONE BEAM CT IMAGES BASED ON LEVEL-SETS

Paper Details

Authors:
Bijju Kranthi Veduruparthi, Jayanta Mukherjee, Partha Pratim Das, Mandira Saha, Sriram Prasath, Raj Kumar Shrimali, Soumendranath Ray, Sanjoy Chatterjee
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10 October 2018 - 12:48am
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poster_v2.pdf

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[1] Bijju Kranthi Veduruparthi, Jayanta Mukherjee, Partha Pratim Das, Mandira Saha, Sriram Prasath, Raj Kumar Shrimali, Soumendranath Ray, Sanjoy Chatterjee, "SEGMENTATION OF LUNG TUMOR IN CONE BEAM CT IMAGES BASED ON LEVEL-SETS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3658. Accessed: Oct. 19, 2018.
@article{3658-18,
url = {http://sigport.org/3658},
author = {Bijju Kranthi Veduruparthi; Jayanta Mukherjee; Partha Pratim Das; Mandira Saha; Sriram Prasath; Raj Kumar Shrimali; Soumendranath Ray; Sanjoy Chatterjee },
publisher = {IEEE SigPort},
title = {SEGMENTATION OF LUNG TUMOR IN CONE BEAM CT IMAGES BASED ON LEVEL-SETS},
year = {2018} }
TY - EJOUR
T1 - SEGMENTATION OF LUNG TUMOR IN CONE BEAM CT IMAGES BASED ON LEVEL-SETS
AU - Bijju Kranthi Veduruparthi; Jayanta Mukherjee; Partha Pratim Das; Mandira Saha; Sriram Prasath; Raj Kumar Shrimali; Soumendranath Ray; Sanjoy Chatterjee
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3658
ER -
Bijju Kranthi Veduruparthi, Jayanta Mukherjee, Partha Pratim Das, Mandira Saha, Sriram Prasath, Raj Kumar Shrimali, Soumendranath Ray, Sanjoy Chatterjee. (2018). SEGMENTATION OF LUNG TUMOR IN CONE BEAM CT IMAGES BASED ON LEVEL-SETS. IEEE SigPort. http://sigport.org/3658
Bijju Kranthi Veduruparthi, Jayanta Mukherjee, Partha Pratim Das, Mandira Saha, Sriram Prasath, Raj Kumar Shrimali, Soumendranath Ray, Sanjoy Chatterjee, 2018. SEGMENTATION OF LUNG TUMOR IN CONE BEAM CT IMAGES BASED ON LEVEL-SETS. Available at: http://sigport.org/3658.
Bijju Kranthi Veduruparthi, Jayanta Mukherjee, Partha Pratim Das, Mandira Saha, Sriram Prasath, Raj Kumar Shrimali, Soumendranath Ray, Sanjoy Chatterjee. (2018). "SEGMENTATION OF LUNG TUMOR IN CONE BEAM CT IMAGES BASED ON LEVEL-SETS." Web.
1. Bijju Kranthi Veduruparthi, Jayanta Mukherjee, Partha Pratim Das, Mandira Saha, Sriram Prasath, Raj Kumar Shrimali, Soumendranath Ray, Sanjoy Chatterjee. SEGMENTATION OF LUNG TUMOR IN CONE BEAM CT IMAGES BASED ON LEVEL-SETS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3658

A UNIFIED FRAMEWORK FOR FAULT DETECTION OF FREIGHT TRAIN IMAGES UNDER COMPLEX ENVIRONMENT


This paper proposes a novel unified framework for fault detection of the freight train images based on convolutional neural network (CNN) under complex environment. Firstly, the multi region proposal networks (MRPN) with a set of prior bounding boxes are introduced to achieve high quality fault proposal generation. And then, we apply a linear non-maximum suppression method to retain the most suitable anchor while removing redundant boxes. Finally, a powerful multi-level region-of-interest (ROI) pooling is proposed for proposal classification and accurate detection.

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9 October 2018 - 8:40pm
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ICIP-poster-A UNIFIED FRAMEWORK FOR FAULT DETECTION OF FREIGHT TRAIN IMAGES UNDER COMPLEX ENVIRONMENT.pdf

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[1] , "A UNIFIED FRAMEWORK FOR FAULT DETECTION OF FREIGHT TRAIN IMAGES UNDER COMPLEX ENVIRONMENT", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3657. Accessed: Oct. 19, 2018.
@article{3657-18,
url = {http://sigport.org/3657},
author = { },
publisher = {IEEE SigPort},
title = {A UNIFIED FRAMEWORK FOR FAULT DETECTION OF FREIGHT TRAIN IMAGES UNDER COMPLEX ENVIRONMENT},
year = {2018} }
TY - EJOUR
T1 - A UNIFIED FRAMEWORK FOR FAULT DETECTION OF FREIGHT TRAIN IMAGES UNDER COMPLEX ENVIRONMENT
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3657
ER -
. (2018). A UNIFIED FRAMEWORK FOR FAULT DETECTION OF FREIGHT TRAIN IMAGES UNDER COMPLEX ENVIRONMENT. IEEE SigPort. http://sigport.org/3657
, 2018. A UNIFIED FRAMEWORK FOR FAULT DETECTION OF FREIGHT TRAIN IMAGES UNDER COMPLEX ENVIRONMENT. Available at: http://sigport.org/3657.
. (2018). "A UNIFIED FRAMEWORK FOR FAULT DETECTION OF FREIGHT TRAIN IMAGES UNDER COMPLEX ENVIRONMENT." Web.
1. . A UNIFIED FRAMEWORK FOR FAULT DETECTION OF FREIGHT TRAIN IMAGES UNDER COMPLEX ENVIRONMENT [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3657

On Regression Losses for Depth Estimation


Depth estimation from a single monocular image has reached great performances thanks to recent works based on deep networks. However, as various choices of losses, architectures and experimental conditions are proposed in the literature, it is difficult to establish their respective influence on the performances. In this paper we propose an in-depth study of various losses and experimental conditions for depth regression, on \nyu dataset. From this study we propose a new network for depth estimation combining an encoder-decoder architecture with an adversarial loss.

Paper Details

Authors:
Marcela Carvalho, Bertrand Le Saux, Pauline Trouvé-Peloux, Andrés Almansa, Frédéric Champagnat
Submitted On:
9 October 2018 - 7:18am
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icip2018_id1083.pdf

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[1] Marcela Carvalho, Bertrand Le Saux, Pauline Trouvé-Peloux, Andrés Almansa, Frédéric Champagnat, "On Regression Losses for Depth Estimation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3656. Accessed: Oct. 19, 2018.
@article{3656-18,
url = {http://sigport.org/3656},
author = {Marcela Carvalho; Bertrand Le Saux; Pauline Trouvé-Peloux; Andrés Almansa; Frédéric Champagnat },
publisher = {IEEE SigPort},
title = {On Regression Losses for Depth Estimation},
year = {2018} }
TY - EJOUR
T1 - On Regression Losses for Depth Estimation
AU - Marcela Carvalho; Bertrand Le Saux; Pauline Trouvé-Peloux; Andrés Almansa; Frédéric Champagnat
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3656
ER -
Marcela Carvalho, Bertrand Le Saux, Pauline Trouvé-Peloux, Andrés Almansa, Frédéric Champagnat. (2018). On Regression Losses for Depth Estimation. IEEE SigPort. http://sigport.org/3656
Marcela Carvalho, Bertrand Le Saux, Pauline Trouvé-Peloux, Andrés Almansa, Frédéric Champagnat, 2018. On Regression Losses for Depth Estimation. Available at: http://sigport.org/3656.
Marcela Carvalho, Bertrand Le Saux, Pauline Trouvé-Peloux, Andrés Almansa, Frédéric Champagnat. (2018). "On Regression Losses for Depth Estimation." Web.
1. Marcela Carvalho, Bertrand Le Saux, Pauline Trouvé-Peloux, Andrés Almansa, Frédéric Champagnat. On Regression Losses for Depth Estimation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3656

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: Oct. 19, 2018.
@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

WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING


Brain tumor can be a fatal disease in the world. With the aim of improving survival rates, many computerized algorithms have been proposed to assist the pathologists to make a diagnosis, using Whole Slide Pathology Images (WSI). Most methods focus on performing patch-level classification and aggregating the patch-level results to obtain the image classification. Since not all patches carry diagnostic information, it is thus important for our algorithm to recognize discriminative and non-discriminative patches.

Paper Details

Authors:
Chaoyi Zhang, Yang Song, Donghao Zhang, Sidong Liu, Mei Chen, Weidong Cai
Submitted On:
9 October 2018 - 8:39am
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poster

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paper

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[1] Chaoyi Zhang, Yang Song, Donghao Zhang, Sidong Liu, Mei Chen, Weidong Cai, "WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3654. Accessed: Oct. 19, 2018.
@article{3654-18,
url = {http://sigport.org/3654},
author = {Chaoyi Zhang; Yang Song; Donghao Zhang; Sidong Liu; Mei Chen; Weidong Cai },
publisher = {IEEE SigPort},
title = {WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING},
year = {2018} }
TY - EJOUR
T1 - WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING
AU - Chaoyi Zhang; Yang Song; Donghao Zhang; Sidong Liu; Mei Chen; Weidong Cai
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3654
ER -
Chaoyi Zhang, Yang Song, Donghao Zhang, Sidong Liu, Mei Chen, Weidong Cai. (2018). WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING. IEEE SigPort. http://sigport.org/3654
Chaoyi Zhang, Yang Song, Donghao Zhang, Sidong Liu, Mei Chen, Weidong Cai, 2018. WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING. Available at: http://sigport.org/3654.
Chaoyi Zhang, Yang Song, Donghao Zhang, Sidong Liu, Mei Chen, Weidong Cai. (2018). "WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING." Web.
1. Chaoyi Zhang, Yang Song, Donghao Zhang, Sidong Liu, Mei Chen, Weidong Cai. WHOLE SLIDE IMAGE CLASSIFICATION VIA ITERATIVE PATCH LABELLING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3654

CYCLOPEAN IMAGE BASED STEREOSCOPIC IMAGE QUALITY ASSESSMENT BY USING SPARSE REPRESENTATION

Paper Details

Authors:
Yongli Chang, Sumei Li, Xu Han, Chunping Hou
Submitted On:
8 October 2018 - 11:47pm
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Type:
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the poster of CYCLOPEAN IMAGE BASED STEREOSCOPIC IMAGE QUALITY ASSESSMENT BY USING SPARSE REPRESENTATION

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[1] Yongli Chang, Sumei Li, Xu Han, Chunping Hou, "CYCLOPEAN IMAGE BASED STEREOSCOPIC IMAGE QUALITY ASSESSMENT BY USING SPARSE REPRESENTATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3652. Accessed: Oct. 19, 2018.
@article{3652-18,
url = {http://sigport.org/3652},
author = {Yongli Chang; Sumei Li; Xu Han; Chunping Hou },
publisher = {IEEE SigPort},
title = {CYCLOPEAN IMAGE BASED STEREOSCOPIC IMAGE QUALITY ASSESSMENT BY USING SPARSE REPRESENTATION},
year = {2018} }
TY - EJOUR
T1 - CYCLOPEAN IMAGE BASED STEREOSCOPIC IMAGE QUALITY ASSESSMENT BY USING SPARSE REPRESENTATION
AU - Yongli Chang; Sumei Li; Xu Han; Chunping Hou
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3652
ER -
Yongli Chang, Sumei Li, Xu Han, Chunping Hou. (2018). CYCLOPEAN IMAGE BASED STEREOSCOPIC IMAGE QUALITY ASSESSMENT BY USING SPARSE REPRESENTATION. IEEE SigPort. http://sigport.org/3652
Yongli Chang, Sumei Li, Xu Han, Chunping Hou, 2018. CYCLOPEAN IMAGE BASED STEREOSCOPIC IMAGE QUALITY ASSESSMENT BY USING SPARSE REPRESENTATION. Available at: http://sigport.org/3652.
Yongli Chang, Sumei Li, Xu Han, Chunping Hou. (2018). "CYCLOPEAN IMAGE BASED STEREOSCOPIC IMAGE QUALITY ASSESSMENT BY USING SPARSE REPRESENTATION." Web.
1. Yongli Chang, Sumei Li, Xu Han, Chunping Hou. CYCLOPEAN IMAGE BASED STEREOSCOPIC IMAGE QUALITY ASSESSMENT BY USING SPARSE REPRESENTATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3652

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