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Image, Video, and Multidimensional Signal Processing

STOCHASTIC TRUNCATED WIRTINGER FLOW ALGORITHM FOR PHASE RETRIEVAL USING BOOLEAN CODED APERTURES

samuel pinilla

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Authors:
samuel pinilla
samuel pinilla, camilo noriega, henry arguello
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11 March 2017 - 8:46pm
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[1] samuel pinilla, camilo noriega, henry arguello, "STOCHASTIC TRUNCATED WIRTINGER FLOW ALGORITHM FOR PHASE RETRIEVAL USING BOOLEAN CODED APERTURES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1718. Accessed: Feb. 22, 2019.
@article{1718-17,
url = {http://sigport.org/1718},
author = {samuel pinilla; camilo noriega; henry arguello },
publisher = {IEEE SigPort},
title = {STOCHASTIC TRUNCATED WIRTINGER FLOW ALGORITHM FOR PHASE RETRIEVAL USING BOOLEAN CODED APERTURES},
year = {2017} }
TY - EJOUR
T1 - STOCHASTIC TRUNCATED WIRTINGER FLOW ALGORITHM FOR PHASE RETRIEVAL USING BOOLEAN CODED APERTURES
AU - samuel pinilla; camilo noriega; henry arguello
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1718
ER -
samuel pinilla, camilo noriega, henry arguello. (2017). STOCHASTIC TRUNCATED WIRTINGER FLOW ALGORITHM FOR PHASE RETRIEVAL USING BOOLEAN CODED APERTURES. IEEE SigPort. http://sigport.org/1718
samuel pinilla, camilo noriega, henry arguello, 2017. STOCHASTIC TRUNCATED WIRTINGER FLOW ALGORITHM FOR PHASE RETRIEVAL USING BOOLEAN CODED APERTURES. Available at: http://sigport.org/1718.
samuel pinilla, camilo noriega, henry arguello. (2017). "STOCHASTIC TRUNCATED WIRTINGER FLOW ALGORITHM FOR PHASE RETRIEVAL USING BOOLEAN CODED APERTURES." Web.
1. samuel pinilla, camilo noriega, henry arguello. STOCHASTIC TRUNCATED WIRTINGER FLOW ALGORITHM FOR PHASE RETRIEVAL USING BOOLEAN CODED APERTURES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1718

STOCHASTIC TRUNCATED WIRTINGER FLOW ALGORITHM FOR PHASE RETRIEVAL USING BOOLEAN CODED APERTURES

samuel pinilla

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Authors:
samuel pinilla
samuel pinilla, camilo noriega, henry arguello
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11 March 2017 - 8:45pm
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[1] samuel pinilla, camilo noriega, henry arguello, "STOCHASTIC TRUNCATED WIRTINGER FLOW ALGORITHM FOR PHASE RETRIEVAL USING BOOLEAN CODED APERTURES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1717. Accessed: Feb. 22, 2019.
@article{1717-17,
url = {http://sigport.org/1717},
author = {samuel pinilla; camilo noriega; henry arguello },
publisher = {IEEE SigPort},
title = {STOCHASTIC TRUNCATED WIRTINGER FLOW ALGORITHM FOR PHASE RETRIEVAL USING BOOLEAN CODED APERTURES},
year = {2017} }
TY - EJOUR
T1 - STOCHASTIC TRUNCATED WIRTINGER FLOW ALGORITHM FOR PHASE RETRIEVAL USING BOOLEAN CODED APERTURES
AU - samuel pinilla; camilo noriega; henry arguello
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1717
ER -
samuel pinilla, camilo noriega, henry arguello. (2017). STOCHASTIC TRUNCATED WIRTINGER FLOW ALGORITHM FOR PHASE RETRIEVAL USING BOOLEAN CODED APERTURES. IEEE SigPort. http://sigport.org/1717
samuel pinilla, camilo noriega, henry arguello, 2017. STOCHASTIC TRUNCATED WIRTINGER FLOW ALGORITHM FOR PHASE RETRIEVAL USING BOOLEAN CODED APERTURES. Available at: http://sigport.org/1717.
samuel pinilla, camilo noriega, henry arguello. (2017). "STOCHASTIC TRUNCATED WIRTINGER FLOW ALGORITHM FOR PHASE RETRIEVAL USING BOOLEAN CODED APERTURES." Web.
1. samuel pinilla, camilo noriega, henry arguello. STOCHASTIC TRUNCATED WIRTINGER FLOW ALGORITHM FOR PHASE RETRIEVAL USING BOOLEAN CODED APERTURES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1717

A CONSTRAINED ADAPTIVE SCAN ORDER APPROACH TO TRANSFORM COEFFICIENT ENTROPY CODING

Ching-Han Chiang

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Ching-Han Chiang
Ching-Han Chiang, Jingning Han, Yaowu Xu
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8 March 2017 - 3:15am
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ICASSP 2017 ADAPTIVE SCAN ORDER (4).pdf

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[1] Ching-Han Chiang, Jingning Han, Yaowu Xu, "A CONSTRAINED ADAPTIVE SCAN ORDER APPROACH TO TRANSFORM COEFFICIENT ENTROPY CODING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1700. Accessed: Feb. 22, 2019.
@article{1700-17,
url = {http://sigport.org/1700},
author = {Ching-Han Chiang; Jingning Han; Yaowu Xu },
publisher = {IEEE SigPort},
title = {A CONSTRAINED ADAPTIVE SCAN ORDER APPROACH TO TRANSFORM COEFFICIENT ENTROPY CODING},
year = {2017} }
TY - EJOUR
T1 - A CONSTRAINED ADAPTIVE SCAN ORDER APPROACH TO TRANSFORM COEFFICIENT ENTROPY CODING
AU - Ching-Han Chiang; Jingning Han; Yaowu Xu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1700
ER -
Ching-Han Chiang, Jingning Han, Yaowu Xu. (2017). A CONSTRAINED ADAPTIVE SCAN ORDER APPROACH TO TRANSFORM COEFFICIENT ENTROPY CODING. IEEE SigPort. http://sigport.org/1700
Ching-Han Chiang, Jingning Han, Yaowu Xu, 2017. A CONSTRAINED ADAPTIVE SCAN ORDER APPROACH TO TRANSFORM COEFFICIENT ENTROPY CODING. Available at: http://sigport.org/1700.
Ching-Han Chiang, Jingning Han, Yaowu Xu. (2017). "A CONSTRAINED ADAPTIVE SCAN ORDER APPROACH TO TRANSFORM COEFFICIENT ENTROPY CODING." Web.
1. Ching-Han Chiang, Jingning Han, Yaowu Xu. A CONSTRAINED ADAPTIVE SCAN ORDER APPROACH TO TRANSFORM COEFFICIENT ENTROPY CODING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1700

Pre-processing And Classification Of Hyperspectral Imagery Via Selective Inpainting Presentation

Victoria Chayes

We propose a semi-supervised algorithm for processing and classification of hyperspectral imagery. For initialization, we keep 20% of the data intact, and use Principal Component Analysis to discard voxels from noisier bands and pixels. Then, we use either an Accelerated Proximal Gradient algorithm (APGL), or a modified APGL algorithm with a penalty term for distance between inpainted pixels and endmembers (APGL Hyp), on the initialized datacube to inpaint the missing data. APGL and APGL Hyp are distinguished by performance on datasets with full pixels removed or extreme noise.

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Authors:
Victoria Chayes
Victoria Chayes, Kevin Miller, Rasika Bhalerao, Jiajie Luo, Wei Zhu, Andrea L. Bertozzi, Wenzhi Liao, Stanley Osher
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6 March 2017 - 2:08pm
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ICASSP Presentation.pdf

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[1] Victoria Chayes, Kevin Miller, Rasika Bhalerao, Jiajie Luo, Wei Zhu, Andrea L. Bertozzi, Wenzhi Liao, Stanley Osher, "Pre-processing And Classification Of Hyperspectral Imagery Via Selective Inpainting Presentation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1659. Accessed: Feb. 22, 2019.
@article{1659-17,
url = {http://sigport.org/1659},
author = {Victoria Chayes; Kevin Miller; Rasika Bhalerao; Jiajie Luo; Wei Zhu; Andrea L. Bertozzi; Wenzhi Liao; Stanley Osher },
publisher = {IEEE SigPort},
title = {Pre-processing And Classification Of Hyperspectral Imagery Via Selective Inpainting Presentation},
year = {2017} }
TY - EJOUR
T1 - Pre-processing And Classification Of Hyperspectral Imagery Via Selective Inpainting Presentation
AU - Victoria Chayes; Kevin Miller; Rasika Bhalerao; Jiajie Luo; Wei Zhu; Andrea L. Bertozzi; Wenzhi Liao; Stanley Osher
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1659
ER -
Victoria Chayes, Kevin Miller, Rasika Bhalerao, Jiajie Luo, Wei Zhu, Andrea L. Bertozzi, Wenzhi Liao, Stanley Osher. (2017). Pre-processing And Classification Of Hyperspectral Imagery Via Selective Inpainting Presentation. IEEE SigPort. http://sigport.org/1659
Victoria Chayes, Kevin Miller, Rasika Bhalerao, Jiajie Luo, Wei Zhu, Andrea L. Bertozzi, Wenzhi Liao, Stanley Osher, 2017. Pre-processing And Classification Of Hyperspectral Imagery Via Selective Inpainting Presentation. Available at: http://sigport.org/1659.
Victoria Chayes, Kevin Miller, Rasika Bhalerao, Jiajie Luo, Wei Zhu, Andrea L. Bertozzi, Wenzhi Liao, Stanley Osher. (2017). "Pre-processing And Classification Of Hyperspectral Imagery Via Selective Inpainting Presentation." Web.
1. Victoria Chayes, Kevin Miller, Rasika Bhalerao, Jiajie Luo, Wei Zhu, Andrea L. Bertozzi, Wenzhi Liao, Stanley Osher. Pre-processing And Classification Of Hyperspectral Imagery Via Selective Inpainting Presentation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1659

FAST HUMAN SEGMENTATION USING COLOR AND DEPTH

Viswanath Gopalakrishnan

Accurate segmentation of humans from live videos is an important problem to be solved in developing immersive video experience. We propose to extract the human segmentation information from color and depth cues in a video using multiple modeling techniques. The prior information from human skeleton data is also fused along with the depth and color models to obtain the final segmentation inside a graph-cut framework. The proposed method runs real time on live videos using single CPU and is shown to be quantitatively outperforming the methods that directly fuse color and depth data.

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Authors:
Viswanath Gopalakrishnan
Raushan Kumar, Rakesh Kumar, Viswanath Gopalakrishnan, Kiran Nanjunda Iyer
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1 March 2017 - 12:07am
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Fast_Human_Segmentation_using_Color_Depth.pdf

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[1] Raushan Kumar, Rakesh Kumar, Viswanath Gopalakrishnan, Kiran Nanjunda Iyer, "FAST HUMAN SEGMENTATION USING COLOR AND DEPTH", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1530. Accessed: Feb. 22, 2019.
@article{1530-17,
url = {http://sigport.org/1530},
author = {Raushan Kumar; Rakesh Kumar; Viswanath Gopalakrishnan; Kiran Nanjunda Iyer },
publisher = {IEEE SigPort},
title = {FAST HUMAN SEGMENTATION USING COLOR AND DEPTH},
year = {2017} }
TY - EJOUR
T1 - FAST HUMAN SEGMENTATION USING COLOR AND DEPTH
AU - Raushan Kumar; Rakesh Kumar; Viswanath Gopalakrishnan; Kiran Nanjunda Iyer
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1530
ER -
Raushan Kumar, Rakesh Kumar, Viswanath Gopalakrishnan, Kiran Nanjunda Iyer. (2017). FAST HUMAN SEGMENTATION USING COLOR AND DEPTH. IEEE SigPort. http://sigport.org/1530
Raushan Kumar, Rakesh Kumar, Viswanath Gopalakrishnan, Kiran Nanjunda Iyer, 2017. FAST HUMAN SEGMENTATION USING COLOR AND DEPTH. Available at: http://sigport.org/1530.
Raushan Kumar, Rakesh Kumar, Viswanath Gopalakrishnan, Kiran Nanjunda Iyer. (2017). "FAST HUMAN SEGMENTATION USING COLOR AND DEPTH." Web.
1. Raushan Kumar, Rakesh Kumar, Viswanath Gopalakrishnan, Kiran Nanjunda Iyer. FAST HUMAN SEGMENTATION USING COLOR AND DEPTH [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1530

RECONSTRUCTION OF 3D SURFACE FROM 2D HOLOGRAPHIC SIGNAL BASED ON KALMAN FILTER

Yangyang Xu

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Authors:
Yangyang Xu
Yun He
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28 February 2017 - 5:10am
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RECONSTRUCTION OF 3D SURFACE FROM 2D HOLOGRAPHIC SIGNAL BASED ON KALMAN FILTER.pdf

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[1] Yun He, "RECONSTRUCTION OF 3D SURFACE FROM 2D HOLOGRAPHIC SIGNAL BASED ON KALMAN FILTER", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1477. Accessed: Feb. 22, 2019.
@article{1477-17,
url = {http://sigport.org/1477},
author = {Yun He },
publisher = {IEEE SigPort},
title = {RECONSTRUCTION OF 3D SURFACE FROM 2D HOLOGRAPHIC SIGNAL BASED ON KALMAN FILTER},
year = {2017} }
TY - EJOUR
T1 - RECONSTRUCTION OF 3D SURFACE FROM 2D HOLOGRAPHIC SIGNAL BASED ON KALMAN FILTER
AU - Yun He
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1477
ER -
Yun He. (2017). RECONSTRUCTION OF 3D SURFACE FROM 2D HOLOGRAPHIC SIGNAL BASED ON KALMAN FILTER. IEEE SigPort. http://sigport.org/1477
Yun He, 2017. RECONSTRUCTION OF 3D SURFACE FROM 2D HOLOGRAPHIC SIGNAL BASED ON KALMAN FILTER. Available at: http://sigport.org/1477.
Yun He. (2017). "RECONSTRUCTION OF 3D SURFACE FROM 2D HOLOGRAPHIC SIGNAL BASED ON KALMAN FILTER." Web.
1. Yun He. RECONSTRUCTION OF 3D SURFACE FROM 2D HOLOGRAPHIC SIGNAL BASED ON KALMAN FILTER [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1477

Image Denoising via Group Sparsity Residual Constraint

Zhiyuan Zha

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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Authors:
Zhiyuan Zha
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11 March 2017 - 8:49pm
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123

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[1] , "Image Denoising via Group Sparsity Residual Constraint", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1473. Accessed: Feb. 22, 2019.
@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

Shunyao Li

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Authors:
Shunyao Li
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: Feb. 22, 2019.
@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

Yalong Yang
Ju Zhang

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:
Yalong Yang
Ju Zhang
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: Feb. 22, 2019.
@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

Arthur Leclaire

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
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: Feb. 22, 2019.
@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

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