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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: Nov. 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

Using Portable Physiological Sensors to Estimate Energy Cost for ‘Body-in-the-Loop’ Optimization of Assistive Robotic Devices

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Authors:
Kimberly Ingraham, Daniel Ferris, C. David Remy
Submitted On:
14 November 2017 - 9:36am
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Ingraham_GlobalSIP_18min_pdf.pdf

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[1] Kimberly Ingraham, Daniel Ferris, C. David Remy, "Using Portable Physiological Sensors to Estimate Energy Cost for ‘Body-in-the-Loop’ Optimization of Assistive Robotic Devices", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2350. Accessed: Nov. 20, 2017.
@article{2350-17,
url = {http://sigport.org/2350},
author = {Kimberly Ingraham; Daniel Ferris; C. David Remy },
publisher = {IEEE SigPort},
title = {Using Portable Physiological Sensors to Estimate Energy Cost for ‘Body-in-the-Loop’ Optimization of Assistive Robotic Devices},
year = {2017} }
TY - EJOUR
T1 - Using Portable Physiological Sensors to Estimate Energy Cost for ‘Body-in-the-Loop’ Optimization of Assistive Robotic Devices
AU - Kimberly Ingraham; Daniel Ferris; C. David Remy
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2350
ER -
Kimberly Ingraham, Daniel Ferris, C. David Remy. (2017). Using Portable Physiological Sensors to Estimate Energy Cost for ‘Body-in-the-Loop’ Optimization of Assistive Robotic Devices. IEEE SigPort. http://sigport.org/2350
Kimberly Ingraham, Daniel Ferris, C. David Remy, 2017. Using Portable Physiological Sensors to Estimate Energy Cost for ‘Body-in-the-Loop’ Optimization of Assistive Robotic Devices. Available at: http://sigport.org/2350.
Kimberly Ingraham, Daniel Ferris, C. David Remy. (2017). "Using Portable Physiological Sensors to Estimate Energy Cost for ‘Body-in-the-Loop’ Optimization of Assistive Robotic Devices." Web.
1. Kimberly Ingraham, Daniel Ferris, C. David Remy. Using Portable Physiological Sensors to Estimate Energy Cost for ‘Body-in-the-Loop’ Optimization of Assistive Robotic Devices [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2350

Performance Evaluation of Beacons for Indoor Localization in Smart Buildings

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Authors:
Andrew Mackey, Petros Spachos
Submitted On:
12 November 2017 - 8:44pm
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mackeyGlobalSIP_2017.pdf

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[1] Andrew Mackey, Petros Spachos, "Performance Evaluation of Beacons for Indoor Localization in Smart Buildings", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2324. Accessed: Nov. 20, 2017.
@article{2324-17,
url = {http://sigport.org/2324},
author = {Andrew Mackey; Petros Spachos },
publisher = {IEEE SigPort},
title = {Performance Evaluation of Beacons for Indoor Localization in Smart Buildings},
year = {2017} }
TY - EJOUR
T1 - Performance Evaluation of Beacons for Indoor Localization in Smart Buildings
AU - Andrew Mackey; Petros Spachos
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2324
ER -
Andrew Mackey, Petros Spachos. (2017). Performance Evaluation of Beacons for Indoor Localization in Smart Buildings. IEEE SigPort. http://sigport.org/2324
Andrew Mackey, Petros Spachos, 2017. Performance Evaluation of Beacons for Indoor Localization in Smart Buildings. Available at: http://sigport.org/2324.
Andrew Mackey, Petros Spachos. (2017). "Performance Evaluation of Beacons for Indoor Localization in Smart Buildings." Web.
1. Andrew Mackey, Petros Spachos. Performance Evaluation of Beacons for Indoor Localization in Smart Buildings [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2324

Recognition of Spoofed Voice Using Convolutional Neural Networks

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9 November 2017 - 9:32pm
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poster_GlobalSIP2017.pdf

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[1] , "Recognition of Spoofed Voice Using Convolutional Neural Networks", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2278. Accessed: Nov. 20, 2017.
@article{2278-17,
url = {http://sigport.org/2278},
author = { },
publisher = {IEEE SigPort},
title = {Recognition of Spoofed Voice Using Convolutional Neural Networks},
year = {2017} }
TY - EJOUR
T1 - Recognition of Spoofed Voice Using Convolutional Neural Networks
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2278
ER -
. (2017). Recognition of Spoofed Voice Using Convolutional Neural Networks. IEEE SigPort. http://sigport.org/2278
, 2017. Recognition of Spoofed Voice Using Convolutional Neural Networks. Available at: http://sigport.org/2278.
. (2017). "Recognition of Spoofed Voice Using Convolutional Neural Networks." Web.
1. . Recognition of Spoofed Voice Using Convolutional Neural Networks [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2278

Compressive Image Recovery using Recurrent Generative Model


Reconstruction of signals from compressively sensed measurements is an ill-posed problem. In this paper, we leverage the recurrent generative model, RIDE, as an image prior for compressive image reconstruction. Recurrent networks can model long-range dependencies in images and hence are suitable to handle global multiplexing in reconstruction from compressive imaging. We perform MAP inference with RIDE using back-propagation to the inputs and projected gradient method. We propose an entropy thresholding based approach for preserving texture in images well.

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Authors:
Akshat Dave, Anil Kumar Vadathya, Kaushik Mitra
Submitted On:
26 September 2017 - 7:44am
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icip17_final.pptx

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[1] Akshat Dave, Anil Kumar Vadathya, Kaushik Mitra, "Compressive Image Recovery using Recurrent Generative Model", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2246. Accessed: Nov. 20, 2017.
@article{2246-17,
url = {http://sigport.org/2246},
author = {Akshat Dave; Anil Kumar Vadathya; Kaushik Mitra },
publisher = {IEEE SigPort},
title = {Compressive Image Recovery using Recurrent Generative Model},
year = {2017} }
TY - EJOUR
T1 - Compressive Image Recovery using Recurrent Generative Model
AU - Akshat Dave; Anil Kumar Vadathya; Kaushik Mitra
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2246
ER -
Akshat Dave, Anil Kumar Vadathya, Kaushik Mitra. (2017). Compressive Image Recovery using Recurrent Generative Model. IEEE SigPort. http://sigport.org/2246
Akshat Dave, Anil Kumar Vadathya, Kaushik Mitra, 2017. Compressive Image Recovery using Recurrent Generative Model. Available at: http://sigport.org/2246.
Akshat Dave, Anil Kumar Vadathya, Kaushik Mitra. (2017). "Compressive Image Recovery using Recurrent Generative Model." Web.
1. Akshat Dave, Anil Kumar Vadathya, Kaushik Mitra. Compressive Image Recovery using Recurrent Generative Model [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2246

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
Submitted On:
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: Nov. 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
Submitted On:
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/2209. Accessed: Nov. 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
Submitted On:
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: Nov. 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: Nov. 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,

Paper Details

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: Nov. 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

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