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Image, Video, and Multidimensional Signal Processing

Image Denoising via Group Sparsity Residual Constraint


Group sparsity or nonlocal image representation has shown great potential in image denoising. However, most existing methods only consider the nonlocal self-similarity (NSS) prior of noisy input image, that is, the similar patches collected only from degraded input, which makes the quality of image denoising largely depend on the input itself. In this paper we propose a new prior model for image denoising, called group sparsity residual constraint (GSRC).

ICASSP--2017.pdf

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11 March 2017 - 8:49pm
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[1] , "Image Denoising via Group Sparsity Residual Constraint", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1473. Accessed: Sep. 21, 2017.
@article{1473-17,
url = {http://sigport.org/1473},
author = { },
publisher = {IEEE SigPort},
title = {Image Denoising via Group Sparsity Residual Constraint},
year = {2017} }
TY - EJOUR
T1 - Image Denoising via Group Sparsity Residual Constraint
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1473
ER -
. (2017). Image Denoising via Group Sparsity Residual Constraint. IEEE SigPort. http://sigport.org/1473
, 2017. Image Denoising via Group Sparsity Residual Constraint. Available at: http://sigport.org/1473.
. (2017). "Image Denoising via Group Sparsity Residual Constraint." Web.
1. . Image Denoising via Group Sparsity Residual Constraint [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1473

Transform Domain Temporal Prediction with Extended Blocks

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Authors:
Shunyao Li, Tejaswi Nanjundaswamy, Kenneth Rose
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28 March 2016 - 7:45pm
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Transform_Domain_Temporal_Prediction_with_Extended_Blocks.pdf

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[1] Shunyao Li, Tejaswi Nanjundaswamy, Kenneth Rose, "Transform Domain Temporal Prediction with Extended Blocks", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1060. Accessed: Sep. 21, 2017.
@article{1060-16,
url = {http://sigport.org/1060},
author = {Shunyao Li; Tejaswi Nanjundaswamy; Kenneth Rose },
publisher = {IEEE SigPort},
title = {Transform Domain Temporal Prediction with Extended Blocks},
year = {2016} }
TY - EJOUR
T1 - Transform Domain Temporal Prediction with Extended Blocks
AU - Shunyao Li; Tejaswi Nanjundaswamy; Kenneth Rose
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1060
ER -
Shunyao Li, Tejaswi Nanjundaswamy, Kenneth Rose. (2016). Transform Domain Temporal Prediction with Extended Blocks. IEEE SigPort. http://sigport.org/1060
Shunyao Li, Tejaswi Nanjundaswamy, Kenneth Rose, 2016. Transform Domain Temporal Prediction with Extended Blocks. Available at: http://sigport.org/1060.
Shunyao Li, Tejaswi Nanjundaswamy, Kenneth Rose. (2016). "Transform Domain Temporal Prediction with Extended Blocks." Web.
1. Shunyao Li, Tejaswi Nanjundaswamy, Kenneth Rose. Transform Domain Temporal Prediction with Extended Blocks [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1060

CONTINUOUS ULTRASOUND BASED TONGUE MOVEMENT VIDEO SYNTHESIS FROM SPEECH


The movement of tongue plays an important role in pronunciation. Visualizing the movement of tongue can improve speech intelligibility and also helps learning a second language. However, hardly any research has been investigated for this topic. In this paper, a framework to synthesize continuous ultrasound tongue movement video from speech is presented. Two different mapping methods are introduced as the most important parts of the framework.

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Authors:
Jianrong Wang; Yalong Yang, Jianguo Wei, Ju Zhang
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29 March 2016 - 10:25pm
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icassp-2016-ultrasound.pdf

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[1] Jianrong Wang; Yalong Yang, Jianguo Wei, Ju Zhang, "CONTINUOUS ULTRASOUND BASED TONGUE MOVEMENT VIDEO SYNTHESIS FROM SPEECH", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1052. Accessed: Sep. 21, 2017.
@article{1052-16,
url = {http://sigport.org/1052},
author = {Jianrong Wang; Yalong Yang; Jianguo Wei; Ju Zhang },
publisher = {IEEE SigPort},
title = {CONTINUOUS ULTRASOUND BASED TONGUE MOVEMENT VIDEO SYNTHESIS FROM SPEECH},
year = {2016} }
TY - EJOUR
T1 - CONTINUOUS ULTRASOUND BASED TONGUE MOVEMENT VIDEO SYNTHESIS FROM SPEECH
AU - Jianrong Wang; Yalong Yang; Jianguo Wei; Ju Zhang
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1052
ER -
Jianrong Wang; Yalong Yang, Jianguo Wei, Ju Zhang. (2016). CONTINUOUS ULTRASOUND BASED TONGUE MOVEMENT VIDEO SYNTHESIS FROM SPEECH. IEEE SigPort. http://sigport.org/1052
Jianrong Wang; Yalong Yang, Jianguo Wei, Ju Zhang, 2016. CONTINUOUS ULTRASOUND BASED TONGUE MOVEMENT VIDEO SYNTHESIS FROM SPEECH. Available at: http://sigport.org/1052.
Jianrong Wang; Yalong Yang, Jianguo Wei, Ju Zhang. (2016). "CONTINUOUS ULTRASOUND BASED TONGUE MOVEMENT VIDEO SYNTHESIS FROM SPEECH." Web.
1. Jianrong Wang; Yalong Yang, Jianguo Wei, Ju Zhang. CONTINUOUS ULTRASOUND BASED TONGUE MOVEMENT VIDEO SYNTHESIS FROM SPEECH [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1052

Microtexture Inpainting through Gaussian Conditional Simulation


Image inpainting consists in filling missing regions of an image by inferring from the surrounding content.
In the case of texture images, inpainting can be formulated in terms of conditional simulation of a stochastic texture model.
Many texture synthesis methods thus have been adapted to texture inpainting, but these methods do not offer theoretical guarantees since the conditional sampling is in general only approximate.

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Authors:
Arthur Leclaire, Bruno Galerne, Lionel Moisan
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23 March 2016 - 8:12am
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[1] Arthur Leclaire, Bruno Galerne, Lionel Moisan, "Microtexture Inpainting through Gaussian Conditional Simulation", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/989. Accessed: Sep. 21, 2017.
@article{989-16,
url = {http://sigport.org/989},
author = {Arthur Leclaire; Bruno Galerne; Lionel Moisan },
publisher = {IEEE SigPort},
title = {Microtexture Inpainting through Gaussian Conditional Simulation},
year = {2016} }
TY - EJOUR
T1 - Microtexture Inpainting through Gaussian Conditional Simulation
AU - Arthur Leclaire; Bruno Galerne; Lionel Moisan
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/989
ER -
Arthur Leclaire, Bruno Galerne, Lionel Moisan. (2016). Microtexture Inpainting through Gaussian Conditional Simulation. IEEE SigPort. http://sigport.org/989
Arthur Leclaire, Bruno Galerne, Lionel Moisan, 2016. Microtexture Inpainting through Gaussian Conditional Simulation. Available at: http://sigport.org/989.
Arthur Leclaire, Bruno Galerne, Lionel Moisan. (2016). "Microtexture Inpainting through Gaussian Conditional Simulation." Web.
1. Arthur Leclaire, Bruno Galerne, Lionel Moisan. Microtexture Inpainting through Gaussian Conditional Simulation [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/989

An improved local binary pattern operator for texture classification

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Fuxiang Lu, Jun Huang
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23 March 2016 - 4:25am
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icassp2016_presentation_lu.pdf

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[1] Fuxiang Lu, Jun Huang, "An improved local binary pattern operator for texture classification", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/984. Accessed: Sep. 21, 2017.
@article{984-16,
url = {http://sigport.org/984},
author = {Fuxiang Lu; Jun Huang },
publisher = {IEEE SigPort},
title = {An improved local binary pattern operator for texture classification},
year = {2016} }
TY - EJOUR
T1 - An improved local binary pattern operator for texture classification
AU - Fuxiang Lu; Jun Huang
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/984
ER -
Fuxiang Lu, Jun Huang. (2016). An improved local binary pattern operator for texture classification. IEEE SigPort. http://sigport.org/984
Fuxiang Lu, Jun Huang, 2016. An improved local binary pattern operator for texture classification. Available at: http://sigport.org/984.
Fuxiang Lu, Jun Huang. (2016). "An improved local binary pattern operator for texture classification." Web.
1. Fuxiang Lu, Jun Huang. An improved local binary pattern operator for texture classification [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/984

Mining Representative Actions for Actor Identification


Previous works on actor identification mainly focused on static
features based on face identification and costume detection,
without considering the abundant dynamic information contained
in videos. In this paper, we propose a novel method
to mine representative actions of each actor, and show the remarkable
power of such actions for actor identification task.
Videos are firstly divided into shots and represented by BoW
based on spatial-temporal features. Then we integrate the prototype

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21 March 2016 - 6:37pm
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Mining Representative Actions for Actor Identification - wlxie.pdf

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[1] , "Mining Representative Actions for Actor Identification", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/938. Accessed: Sep. 21, 2017.
@article{938-16,
url = {http://sigport.org/938},
author = { },
publisher = {IEEE SigPort},
title = {Mining Representative Actions for Actor Identification},
year = {2016} }
TY - EJOUR
T1 - Mining Representative Actions for Actor Identification
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/938
ER -
. (2016). Mining Representative Actions for Actor Identification. IEEE SigPort. http://sigport.org/938
, 2016. Mining Representative Actions for Actor Identification. Available at: http://sigport.org/938.
. (2016). "Mining Representative Actions for Actor Identification." Web.
1. . Mining Representative Actions for Actor Identification [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/938

NONCONVEX COMPRESSIVE SENSING RECONSTRUCTION FOR TENSOR USING STRUCTURES IN MODES


poster.pdf

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Authors:
Xin Ding, Wei Chen, Ian Wassell
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21 March 2016 - 6:33am
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poster.pdf

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[1] Xin Ding, Wei Chen, Ian Wassell, "NONCONVEX COMPRESSIVE SENSING RECONSTRUCTION FOR TENSOR USING STRUCTURES IN MODES", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/921. Accessed: Sep. 21, 2017.
@article{921-16,
url = {http://sigport.org/921},
author = {Xin Ding; Wei Chen; Ian Wassell },
publisher = {IEEE SigPort},
title = {NONCONVEX COMPRESSIVE SENSING RECONSTRUCTION FOR TENSOR USING STRUCTURES IN MODES},
year = {2016} }
TY - EJOUR
T1 - NONCONVEX COMPRESSIVE SENSING RECONSTRUCTION FOR TENSOR USING STRUCTURES IN MODES
AU - Xin Ding; Wei Chen; Ian Wassell
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/921
ER -
Xin Ding, Wei Chen, Ian Wassell. (2016). NONCONVEX COMPRESSIVE SENSING RECONSTRUCTION FOR TENSOR USING STRUCTURES IN MODES. IEEE SigPort. http://sigport.org/921
Xin Ding, Wei Chen, Ian Wassell, 2016. NONCONVEX COMPRESSIVE SENSING RECONSTRUCTION FOR TENSOR USING STRUCTURES IN MODES. Available at: http://sigport.org/921.
Xin Ding, Wei Chen, Ian Wassell. (2016). "NONCONVEX COMPRESSIVE SENSING RECONSTRUCTION FOR TENSOR USING STRUCTURES IN MODES." Web.
1. Xin Ding, Wei Chen, Ian Wassell. NONCONVEX COMPRESSIVE SENSING RECONSTRUCTION FOR TENSOR USING STRUCTURES IN MODES [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/921

Manga-Specific Features and Latent Style Model for Manga Style Analysis


A latent style model describing manga styles based on the proposed manga-specific features is constructed to facilitate novel style-based applications. Two manga-specific features, i.e., screentone features showing texture and shade, and panel features showing panel arrangement, are firstly proposed to describe manga pages. Based on the latent Dirichlet allocation technique, we discover latent style elements embedded in manga documents, which are described by visual words derived from manga-specific features.

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20 March 2016 - 8:45pm
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Manga-Specific Features and Latent Style Model for Manga Style Analysis.pdf

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[1] , "Manga-Specific Features and Latent Style Model for Manga Style Analysis", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/896. Accessed: Sep. 21, 2017.
@article{896-16,
url = {http://sigport.org/896},
author = { },
publisher = {IEEE SigPort},
title = {Manga-Specific Features and Latent Style Model for Manga Style Analysis},
year = {2016} }
TY - EJOUR
T1 - Manga-Specific Features and Latent Style Model for Manga Style Analysis
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/896
ER -
. (2016). Manga-Specific Features and Latent Style Model for Manga Style Analysis. IEEE SigPort. http://sigport.org/896
, 2016. Manga-Specific Features and Latent Style Model for Manga Style Analysis. Available at: http://sigport.org/896.
. (2016). "Manga-Specific Features and Latent Style Model for Manga Style Analysis." Web.
1. . Manga-Specific Features and Latent Style Model for Manga Style Analysis [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/896

News Story Clustering with Fisher Embedding


An automatic news story clustering system is presented to facilitate efficient news browsing and summarization. We describe news content by considering both what objects appear and how these objects move in news stories. With Fisher embedding, we respectively encode local features, semantics features, and dense trajectories as Fisher vectors, based on which similarity between news stories can be well evaluated and thus better clustering performance can be obtained.

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20 March 2016 - 8:46pm
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News Story Clustering with Fisher Embedding.pdf

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[1] , "News Story Clustering with Fisher Embedding", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/895. Accessed: Sep. 21, 2017.
@article{895-16,
url = {http://sigport.org/895},
author = { },
publisher = {IEEE SigPort},
title = {News Story Clustering with Fisher Embedding},
year = {2016} }
TY - EJOUR
T1 - News Story Clustering with Fisher Embedding
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/895
ER -
. (2016). News Story Clustering with Fisher Embedding. IEEE SigPort. http://sigport.org/895
, 2016. News Story Clustering with Fisher Embedding. Available at: http://sigport.org/895.
. (2016). "News Story Clustering with Fisher Embedding." Web.
1. . News Story Clustering with Fisher Embedding [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/895

Intrinsic Two-Dimensional Local Structures for Micro-Expression Recognition


Intrinsic two-dimensional local structures

An elapsed facial emotion involves changes of facial contour due to the motions (such as contraction or stretch) of facial muscles located at the eyes, nose, lips and etc. Thus, the important information such as corners of facial contours that are located in various regions of the face are crucial to the recognition of facial expressions, and even more apparent for micro-expressions. In this paper, we propose the first known notion of employing intrinsic two-dimensional (i2D) local structures to represent these features for micro-expression recognition.

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Authors:
Yee-Hui Oh, Anh Cat Le Ngo, Raphael Chung-Wei Phan, John See, Huo-Chong Ling
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20 March 2016 - 12:16pm
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mi2dbp_icassp2016.pdf

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[1] Yee-Hui Oh, Anh Cat Le Ngo, Raphael Chung-Wei Phan, John See, Huo-Chong Ling, "Intrinsic Two-Dimensional Local Structures for Micro-Expression Recognition", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/886. Accessed: Sep. 21, 2017.
@article{886-16,
url = {http://sigport.org/886},
author = {Yee-Hui Oh; Anh Cat Le Ngo; Raphael Chung-Wei Phan; John See; Huo-Chong Ling },
publisher = {IEEE SigPort},
title = {Intrinsic Two-Dimensional Local Structures for Micro-Expression Recognition},
year = {2016} }
TY - EJOUR
T1 - Intrinsic Two-Dimensional Local Structures for Micro-Expression Recognition
AU - Yee-Hui Oh; Anh Cat Le Ngo; Raphael Chung-Wei Phan; John See; Huo-Chong Ling
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/886
ER -
Yee-Hui Oh, Anh Cat Le Ngo, Raphael Chung-Wei Phan, John See, Huo-Chong Ling. (2016). Intrinsic Two-Dimensional Local Structures for Micro-Expression Recognition. IEEE SigPort. http://sigport.org/886
Yee-Hui Oh, Anh Cat Le Ngo, Raphael Chung-Wei Phan, John See, Huo-Chong Ling, 2016. Intrinsic Two-Dimensional Local Structures for Micro-Expression Recognition. Available at: http://sigport.org/886.
Yee-Hui Oh, Anh Cat Le Ngo, Raphael Chung-Wei Phan, John See, Huo-Chong Ling. (2016). "Intrinsic Two-Dimensional Local Structures for Micro-Expression Recognition." Web.
1. Yee-Hui Oh, Anh Cat Le Ngo, Raphael Chung-Wei Phan, John See, Huo-Chong Ling. Intrinsic Two-Dimensional Local Structures for Micro-Expression Recognition [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/886

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