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Audio and Acoustic Signal Processing

Natural Sound Rendering for Headphones: Integration of signal processing techniques (slides)


With the strong growth of assistive and personal listening devices, natural sound rendering over headphones is becoming a necessity for prolonged listening in multimedia and virtual reality applications. The aim of natural sound rendering is to naturally recreate the sound scenes with the spatial and timbral quality as natural as possible, so as to achieve a truly immersive listening experience. However, rendering natural sound over headphones encounters many challenges. This tutorial article presents signal processing techniques to tackle these challenges to assist human listening.

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Authors:
Kaushik Sunder, Ee-Leng Tan
Submitted On:
23 February 2016 - 1:43pm
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SPM15slides_Natural Sound Rendering for Headphones.pdf

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[1] Kaushik Sunder, Ee-Leng Tan, "Natural Sound Rendering for Headphones: Integration of signal processing techniques (slides)", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/167. Accessed: Sep. 20, 2017.
@article{167-15,
url = {http://sigport.org/167},
author = {Kaushik Sunder; Ee-Leng Tan },
publisher = {IEEE SigPort},
title = {Natural Sound Rendering for Headphones: Integration of signal processing techniques (slides)},
year = {2015} }
TY - EJOUR
T1 - Natural Sound Rendering for Headphones: Integration of signal processing techniques (slides)
AU - Kaushik Sunder; Ee-Leng Tan
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/167
ER -
Kaushik Sunder, Ee-Leng Tan. (2015). Natural Sound Rendering for Headphones: Integration of signal processing techniques (slides). IEEE SigPort. http://sigport.org/167
Kaushik Sunder, Ee-Leng Tan, 2015. Natural Sound Rendering for Headphones: Integration of signal processing techniques (slides). Available at: http://sigport.org/167.
Kaushik Sunder, Ee-Leng Tan. (2015). "Natural Sound Rendering for Headphones: Integration of signal processing techniques (slides)." Web.
1. Kaushik Sunder, Ee-Leng Tan. Natural Sound Rendering for Headphones: Integration of signal processing techniques (slides) [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/167

Multi-Glimpse LSTM with Color-Depth Feature Fusion for Human Detection


With the development of depth cameras such as Kinect and Intel Realsense, RGB-D based human detection receives continuous research attention due to its usage in a variety of applications. In this paper, we propose a new Multi-Glimpse LSTM (MG-LSTM) network, in which multi-scale contextual information is sequentially integrated to promote the human detection performance. Furthermore, we propose a feature fusion strategy based on our MG-LSTM network to better incorporate the RGB and depth information.

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Authors:
Hengduo Li, Jun Liu, Guyue Zhang, Yuan Gao, Yirui Wu
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18 September 2017 - 2:20am
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mg-lstm Hengduo

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[1] Hengduo Li, Jun Liu, Guyue Zhang, Yuan Gao, Yirui Wu, "Multi-Glimpse LSTM with Color-Depth Feature Fusion for Human Detection", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2220. Accessed: Sep. 20, 2017.
@article{2220-17,
url = {http://sigport.org/2220},
author = {Hengduo Li; Jun Liu; Guyue Zhang; Yuan Gao; Yirui Wu },
publisher = {IEEE SigPort},
title = {Multi-Glimpse LSTM with Color-Depth Feature Fusion for Human Detection},
year = {2017} }
TY - EJOUR
T1 - Multi-Glimpse LSTM with Color-Depth Feature Fusion for Human Detection
AU - Hengduo Li; Jun Liu; Guyue Zhang; Yuan Gao; Yirui Wu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2220
ER -
Hengduo Li, Jun Liu, Guyue Zhang, Yuan Gao, Yirui Wu. (2017). Multi-Glimpse LSTM with Color-Depth Feature Fusion for Human Detection. IEEE SigPort. http://sigport.org/2220
Hengduo Li, Jun Liu, Guyue Zhang, Yuan Gao, Yirui Wu, 2017. Multi-Glimpse LSTM with Color-Depth Feature Fusion for Human Detection. Available at: http://sigport.org/2220.
Hengduo Li, Jun Liu, Guyue Zhang, Yuan Gao, Yirui Wu. (2017). "Multi-Glimpse LSTM with Color-Depth Feature Fusion for Human Detection." Web.
1. Hengduo Li, Jun Liu, Guyue Zhang, Yuan Gao, Yirui Wu. Multi-Glimpse LSTM with Color-Depth Feature Fusion for Human Detection [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2220

DEFORMATION TRANSFER OF 3D HUMAN SHAPES AND POSES ON MANIFOLDS

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Authors:
Abd El Rahman Shabayek, Djamila Aouada, Alexandre Saint, Björn Ottersten
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17 September 2017 - 2:00am
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[1] Abd El Rahman Shabayek, Djamila Aouada, Alexandre Saint, Björn Ottersten , "DEFORMATION TRANSFER OF 3D HUMAN SHAPES AND POSES ON MANIFOLDS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2209. Accessed: Sep. 20, 2017.
@article{2209-17,
url = {http://sigport.org/2209},
author = {Abd El Rahman Shabayek; Djamila Aouada; Alexandre Saint; Björn Ottersten },
publisher = {IEEE SigPort},
title = {DEFORMATION TRANSFER OF 3D HUMAN SHAPES AND POSES ON MANIFOLDS},
year = {2017} }
TY - EJOUR
T1 - DEFORMATION TRANSFER OF 3D HUMAN SHAPES AND POSES ON MANIFOLDS
AU - Abd El Rahman Shabayek; Djamila Aouada; Alexandre Saint; Björn Ottersten
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2209
ER -
Abd El Rahman Shabayek, Djamila Aouada, Alexandre Saint, Björn Ottersten . (2017). DEFORMATION TRANSFER OF 3D HUMAN SHAPES AND POSES ON MANIFOLDS. IEEE SigPort. http://sigport.org/2209
Abd El Rahman Shabayek, Djamila Aouada, Alexandre Saint, Björn Ottersten , 2017. DEFORMATION TRANSFER OF 3D HUMAN SHAPES AND POSES ON MANIFOLDS. Available at: http://sigport.org/2209.
Abd El Rahman Shabayek, Djamila Aouada, Alexandre Saint, Björn Ottersten . (2017). "DEFORMATION TRANSFER OF 3D HUMAN SHAPES AND POSES ON MANIFOLDS." Web.
1. Abd El Rahman Shabayek, Djamila Aouada, Alexandre Saint, Björn Ottersten . DEFORMATION TRANSFER OF 3D HUMAN SHAPES AND POSES ON MANIFOLDS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2209

DEFORMATION TRANSFER OF 3D HUMAN SHAPES AND POSES ON MANIFOLDS

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Authors:
Abd El Rahman Shabayek, Djamila Aouada, Alexandre Saint, Björn Ottersten
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17 September 2017 - 2:00am
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Deformation_Transfer_Sep_2017.pdf

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[1] Abd El Rahman Shabayek, Djamila Aouada, Alexandre Saint, Björn Ottersten , "DEFORMATION TRANSFER OF 3D HUMAN SHAPES AND POSES ON MANIFOLDS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2208. Accessed: Sep. 20, 2017.
@article{2208-17,
url = {http://sigport.org/2208},
author = {Abd El Rahman Shabayek; Djamila Aouada; Alexandre Saint; Björn Ottersten },
publisher = {IEEE SigPort},
title = {DEFORMATION TRANSFER OF 3D HUMAN SHAPES AND POSES ON MANIFOLDS},
year = {2017} }
TY - EJOUR
T1 - DEFORMATION TRANSFER OF 3D HUMAN SHAPES AND POSES ON MANIFOLDS
AU - Abd El Rahman Shabayek; Djamila Aouada; Alexandre Saint; Björn Ottersten
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2208
ER -
Abd El Rahman Shabayek, Djamila Aouada, Alexandre Saint, Björn Ottersten . (2017). DEFORMATION TRANSFER OF 3D HUMAN SHAPES AND POSES ON MANIFOLDS. IEEE SigPort. http://sigport.org/2208
Abd El Rahman Shabayek, Djamila Aouada, Alexandre Saint, Björn Ottersten , 2017. DEFORMATION TRANSFER OF 3D HUMAN SHAPES AND POSES ON MANIFOLDS. Available at: http://sigport.org/2208.
Abd El Rahman Shabayek, Djamila Aouada, Alexandre Saint, Björn Ottersten . (2017). "DEFORMATION TRANSFER OF 3D HUMAN SHAPES AND POSES ON MANIFOLDS." Web.
1. Abd El Rahman Shabayek, Djamila Aouada, Alexandre Saint, Björn Ottersten . DEFORMATION TRANSFER OF 3D HUMAN SHAPES AND POSES ON MANIFOLDS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2208

Deep Joint Discriminative Learning for Vehicle Re-identification and Retrieval


In this paper, we propose a novel vehicle re-identification method based on a Deep Joint Discriminative Learning (DJDL) model, which utilizes a deep convolutional network to effectively extract discriminative representations for vehicle images. To exploit properties and relationship among samples in different views, we design a unified framework to combine several different tasks efficiently, including identification, attribute recognition, verification and triplet tasks. The whole network is optimized jointly via a specific batch composition design.

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Authors:
Yuqi Li, Yanghao Li, Hongfei Yan, Jiaying Liu
Submitted On:
16 September 2017 - 10:44am
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ICIP-2017-DJDL-ppt-v2.pdf

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[1] Yuqi Li, Yanghao Li, Hongfei Yan, Jiaying Liu, "Deep Joint Discriminative Learning for Vehicle Re-identification and Retrieval", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2195. Accessed: Sep. 20, 2017.
@article{2195-17,
url = {http://sigport.org/2195},
author = {Yuqi Li; Yanghao Li; Hongfei Yan; Jiaying Liu },
publisher = {IEEE SigPort},
title = {Deep Joint Discriminative Learning for Vehicle Re-identification and Retrieval},
year = {2017} }
TY - EJOUR
T1 - Deep Joint Discriminative Learning for Vehicle Re-identification and Retrieval
AU - Yuqi Li; Yanghao Li; Hongfei Yan; Jiaying Liu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2195
ER -
Yuqi Li, Yanghao Li, Hongfei Yan, Jiaying Liu. (2017). Deep Joint Discriminative Learning for Vehicle Re-identification and Retrieval. IEEE SigPort. http://sigport.org/2195
Yuqi Li, Yanghao Li, Hongfei Yan, Jiaying Liu, 2017. Deep Joint Discriminative Learning for Vehicle Re-identification and Retrieval. Available at: http://sigport.org/2195.
Yuqi Li, Yanghao Li, Hongfei Yan, Jiaying Liu. (2017). "Deep Joint Discriminative Learning for Vehicle Re-identification and Retrieval." Web.
1. Yuqi Li, Yanghao Li, Hongfei Yan, Jiaying Liu. Deep Joint Discriminative Learning for Vehicle Re-identification and Retrieval [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2195

Supervised Evaluation of the Quality of BinaryPartition Trees based on Uncertain Semantic Ground-Truth for Image Segmentation Purpose


The binary partition tree (BPT) is a hierarchical data-structure that
models the content of an image in a multiscale way. In particular,
a cut of the BPT of an image provides a segmentation, as a partition
of the image support. Actually, building a BPT allows for
dramatically reducing the search space for segmentation purposes,
based on intrinsic (image signal) and extrinsic (construction metric)
information. A large literature has been devoted to the construction
on such metrics, and the associated choice of criteria (spectral, spatial,

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Authors:
Jimmy Francky Randrianasoa, Camille Kurtz, Pierre Ganc¸arski, Eric Desjardin, Nicolas Passat
Submitted On:
16 September 2017 - 1:37am
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ICIP-2017-JF-Poster.pdf

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[1] Jimmy Francky Randrianasoa, Camille Kurtz, Pierre Ganc¸arski, Eric Desjardin, Nicolas Passat, "Supervised Evaluation of the Quality of BinaryPartition Trees based on Uncertain Semantic Ground-Truth for Image Segmentation Purpose", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2174. Accessed: Sep. 20, 2017.
@article{2174-17,
url = {http://sigport.org/2174},
author = {Jimmy Francky Randrianasoa; Camille Kurtz; Pierre Ganc¸arski; Eric Desjardin; Nicolas Passat },
publisher = {IEEE SigPort},
title = {Supervised Evaluation of the Quality of BinaryPartition Trees based on Uncertain Semantic Ground-Truth for Image Segmentation Purpose},
year = {2017} }
TY - EJOUR
T1 - Supervised Evaluation of the Quality of BinaryPartition Trees based on Uncertain Semantic Ground-Truth for Image Segmentation Purpose
AU - Jimmy Francky Randrianasoa; Camille Kurtz; Pierre Ganc¸arski; Eric Desjardin; Nicolas Passat
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2174
ER -
Jimmy Francky Randrianasoa, Camille Kurtz, Pierre Ganc¸arski, Eric Desjardin, Nicolas Passat. (2017). Supervised Evaluation of the Quality of BinaryPartition Trees based on Uncertain Semantic Ground-Truth for Image Segmentation Purpose. IEEE SigPort. http://sigport.org/2174
Jimmy Francky Randrianasoa, Camille Kurtz, Pierre Ganc¸arski, Eric Desjardin, Nicolas Passat, 2017. Supervised Evaluation of the Quality of BinaryPartition Trees based on Uncertain Semantic Ground-Truth for Image Segmentation Purpose. Available at: http://sigport.org/2174.
Jimmy Francky Randrianasoa, Camille Kurtz, Pierre Ganc¸arski, Eric Desjardin, Nicolas Passat. (2017). "Supervised Evaluation of the Quality of BinaryPartition Trees based on Uncertain Semantic Ground-Truth for Image Segmentation Purpose." Web.
1. Jimmy Francky Randrianasoa, Camille Kurtz, Pierre Ganc¸arski, Eric Desjardin, Nicolas Passat. Supervised Evaluation of the Quality of BinaryPartition Trees based on Uncertain Semantic Ground-Truth for Image Segmentation Purpose [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2174

DEEP LEARNING ARCHITECTURE FOR PEDESTRIAN 3-D LOCALIZATION AND TRACKING USING MULTIPLE CAMERAS

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Authors:
Byeongho Heo, Moonsub Byeon, Jin Young Choi
Submitted On:
15 September 2017 - 11:10pm
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icip2017_2dpgp_kkk_upload.pptx

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[1] Byeongho Heo, Moonsub Byeon, Jin Young Choi, " DEEP LEARNING ARCHITECTURE FOR PEDESTRIAN 3-D LOCALIZATION AND TRACKING USING MULTIPLE CAMERAS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2167. Accessed: Sep. 20, 2017.
@article{2167-17,
url = {http://sigport.org/2167},
author = {Byeongho Heo; Moonsub Byeon; Jin Young Choi },
publisher = {IEEE SigPort},
title = { DEEP LEARNING ARCHITECTURE FOR PEDESTRIAN 3-D LOCALIZATION AND TRACKING USING MULTIPLE CAMERAS},
year = {2017} }
TY - EJOUR
T1 - DEEP LEARNING ARCHITECTURE FOR PEDESTRIAN 3-D LOCALIZATION AND TRACKING USING MULTIPLE CAMERAS
AU - Byeongho Heo; Moonsub Byeon; Jin Young Choi
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2167
ER -
Byeongho Heo, Moonsub Byeon, Jin Young Choi. (2017). DEEP LEARNING ARCHITECTURE FOR PEDESTRIAN 3-D LOCALIZATION AND TRACKING USING MULTIPLE CAMERAS. IEEE SigPort. http://sigport.org/2167
Byeongho Heo, Moonsub Byeon, Jin Young Choi, 2017. DEEP LEARNING ARCHITECTURE FOR PEDESTRIAN 3-D LOCALIZATION AND TRACKING USING MULTIPLE CAMERAS. Available at: http://sigport.org/2167.
Byeongho Heo, Moonsub Byeon, Jin Young Choi. (2017). " DEEP LEARNING ARCHITECTURE FOR PEDESTRIAN 3-D LOCALIZATION AND TRACKING USING MULTIPLE CAMERAS." Web.
1. Byeongho Heo, Moonsub Byeon, Jin Young Choi. DEEP LEARNING ARCHITECTURE FOR PEDESTRIAN 3-D LOCALIZATION AND TRACKING USING MULTIPLE CAMERAS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2167

MODELING STRUCTURAL DISSIMILARITY BASED ON SHAPE EMBODIMENT FOR CELL SEGMENTATION


Accurate cell segmentation is one of the critical, yet challenging problems in microscopy images due to ambiguous boundaries as well as a wide variation of shapes and sizes of cells. Even though a number of existing methods have achieved decent results for cell segmentation, boundary vagueness between adjoining cells tended to cause generation of perceptually inaccurate segmentation of stained nuclei. We propose

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15 September 2017 - 1:46am
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[1] , "MODELING STRUCTURAL DISSIMILARITY BASED ON SHAPE EMBODIMENT FOR CELL SEGMENTATION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2084. Accessed: Sep. 20, 2017.
@article{2084-17,
url = {http://sigport.org/2084},
author = { },
publisher = {IEEE SigPort},
title = {MODELING STRUCTURAL DISSIMILARITY BASED ON SHAPE EMBODIMENT FOR CELL SEGMENTATION},
year = {2017} }
TY - EJOUR
T1 - MODELING STRUCTURAL DISSIMILARITY BASED ON SHAPE EMBODIMENT FOR CELL SEGMENTATION
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2084
ER -
. (2017). MODELING STRUCTURAL DISSIMILARITY BASED ON SHAPE EMBODIMENT FOR CELL SEGMENTATION. IEEE SigPort. http://sigport.org/2084
, 2017. MODELING STRUCTURAL DISSIMILARITY BASED ON SHAPE EMBODIMENT FOR CELL SEGMENTATION. Available at: http://sigport.org/2084.
. (2017). "MODELING STRUCTURAL DISSIMILARITY BASED ON SHAPE EMBODIMENT FOR CELL SEGMENTATION." Web.
1. . MODELING STRUCTURAL DISSIMILARITY BASED ON SHAPE EMBODIMENT FOR CELL SEGMENTATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2084

MOTION-CONSISTENT VIDEO INPAINTING


This demonstration aims to show some resulting videos for our method presented in ICIP. It is a fast and automatic inpainting technique for high-definition videos which works under many challenging conditions such as a moving camera, a dynamic background or a long-lasting occlusion. By incorporating optical flow in a global patch-based algorithm, our method provide improvements compared to the state-of-the-art, especially in motion preservation.

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Authors:
Andres Almansa, Yann Gousseau, Simon Masnou
Submitted On:
14 September 2017 - 5:32pm
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Poster_ThucTrinhLE_videoinpainting_ICIP.pdf

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[1] Andres Almansa, Yann Gousseau, Simon Masnou, "MOTION-CONSISTENT VIDEO INPAINTING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2051. Accessed: Sep. 20, 2017.
@article{2051-17,
url = {http://sigport.org/2051},
author = {Andres Almansa; Yann Gousseau; Simon Masnou },
publisher = {IEEE SigPort},
title = {MOTION-CONSISTENT VIDEO INPAINTING},
year = {2017} }
TY - EJOUR
T1 - MOTION-CONSISTENT VIDEO INPAINTING
AU - Andres Almansa; Yann Gousseau; Simon Masnou
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2051
ER -
Andres Almansa, Yann Gousseau, Simon Masnou. (2017). MOTION-CONSISTENT VIDEO INPAINTING. IEEE SigPort. http://sigport.org/2051
Andres Almansa, Yann Gousseau, Simon Masnou, 2017. MOTION-CONSISTENT VIDEO INPAINTING. Available at: http://sigport.org/2051.
Andres Almansa, Yann Gousseau, Simon Masnou. (2017). "MOTION-CONSISTENT VIDEO INPAINTING." Web.
1. Andres Almansa, Yann Gousseau, Simon Masnou. MOTION-CONSISTENT VIDEO INPAINTING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2051

GLOBAL MULTIVIEW REGISTRATION USING NON-CONVEX ADMM


We consider the problem of aligning multiview scans obtained using
a range scanner. The computational pipeline for this problem can be
divided into two phases: (i) finding point-to-point correspondences
between overlapping scans, and (ii) registration of the scans based
on the found correspondences. The focus of this work is on global
registration in which the scans (modeled as point clouds) are required
to be jointly registered in a common reference frame. We consider
an optimization framework for global registration that is based on

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Authors:
Kunal N. Chaudhury
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14 September 2017 - 7:08am
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slides.pdf

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[1] Kunal N. Chaudhury, "GLOBAL MULTIVIEW REGISTRATION USING NON-CONVEX ADMM", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2015. Accessed: Sep. 20, 2017.
@article{2015-17,
url = {http://sigport.org/2015},
author = {Kunal N. Chaudhury },
publisher = {IEEE SigPort},
title = {GLOBAL MULTIVIEW REGISTRATION USING NON-CONVEX ADMM},
year = {2017} }
TY - EJOUR
T1 - GLOBAL MULTIVIEW REGISTRATION USING NON-CONVEX ADMM
AU - Kunal N. Chaudhury
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2015
ER -
Kunal N. Chaudhury. (2017). GLOBAL MULTIVIEW REGISTRATION USING NON-CONVEX ADMM. IEEE SigPort. http://sigport.org/2015
Kunal N. Chaudhury, 2017. GLOBAL MULTIVIEW REGISTRATION USING NON-CONVEX ADMM. Available at: http://sigport.org/2015.
Kunal N. Chaudhury. (2017). "GLOBAL MULTIVIEW REGISTRATION USING NON-CONVEX ADMM." Web.
1. Kunal N. Chaudhury. GLOBAL MULTIVIEW REGISTRATION USING NON-CONVEX ADMM [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2015

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