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Multimodal signal processing

An Occlusion Probability Model for Improving the Rendering Quality of Views

Changjian Zhu

Occlusion as a common phenomenon in object surface can seriously affect information collection of light field. To visualize light field data-set, occlusions are usually idealized and neglected for most prior light field rendering (LFR) algorithms. However, the 3D spatial structure of some features may be missing to capture some incorrect samples caused by occlusion discontinuities. To solve this problem, we propose an occlusion probability (OCP) model to improve the capturing information and the rendering quality of views with occlusion for the LFR.

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Authors:
Changjian Zhu
Submitted On:
20 September 2019 - 5:41am
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[1] , "An Occlusion Probability Model for Improving the Rendering Quality of Views", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4772. Accessed: Sep. 20, 2019.
@article{4772-19,
url = {http://sigport.org/4772},
author = { },
publisher = {IEEE SigPort},
title = {An Occlusion Probability Model for Improving the Rendering Quality of Views},
year = {2019} }
TY - EJOUR
T1 - An Occlusion Probability Model for Improving the Rendering Quality of Views
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4772
ER -
. (2019). An Occlusion Probability Model for Improving the Rendering Quality of Views. IEEE SigPort. http://sigport.org/4772
, 2019. An Occlusion Probability Model for Improving the Rendering Quality of Views. Available at: http://sigport.org/4772.
. (2019). "An Occlusion Probability Model for Improving the Rendering Quality of Views." Web.
1. . An Occlusion Probability Model for Improving the Rendering Quality of Views [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4772

FAST: Flow-Assisted Shearlet Transform for Densely-sampled Light Field Reconstruction

Yuan Gao

Shearlet Transform (ST) is one of the most effective methods for Densely-Sampled Light Field (DSLF) reconstruction from a Sparsely-Sampled Light Field (SSLF). However, ST requires a precise disparity estimation of the SSLF. To this end, in this paper a state-of-the-art optical flow method, i.e. PWC-Net, is employed to estimate bidirectional disparity maps between neighboring views in the SSLF. Moreover, to take full advantage of optical flow and ST for DSLF reconstruction, a novel learning-based method, referred to as Flow-Assisted Shearlet Transform (FAST), is proposed in this paper.

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Authors:
Yuan Gao
Reinhard Koch, Robert Bregovic, Atanas Gotchev
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16 September 2019 - 12:24pm
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[1] Reinhard Koch, Robert Bregovic, Atanas Gotchev , "FAST: Flow-Assisted Shearlet Transform for Densely-sampled Light Field Reconstruction", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4643. Accessed: Sep. 20, 2019.
@article{4643-19,
url = {http://sigport.org/4643},
author = {Reinhard Koch; Robert Bregovic; Atanas Gotchev },
publisher = {IEEE SigPort},
title = {FAST: Flow-Assisted Shearlet Transform for Densely-sampled Light Field Reconstruction},
year = {2019} }
TY - EJOUR
T1 - FAST: Flow-Assisted Shearlet Transform for Densely-sampled Light Field Reconstruction
AU - Reinhard Koch; Robert Bregovic; Atanas Gotchev
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4643
ER -
Reinhard Koch, Robert Bregovic, Atanas Gotchev . (2019). FAST: Flow-Assisted Shearlet Transform for Densely-sampled Light Field Reconstruction. IEEE SigPort. http://sigport.org/4643
Reinhard Koch, Robert Bregovic, Atanas Gotchev , 2019. FAST: Flow-Assisted Shearlet Transform for Densely-sampled Light Field Reconstruction. Available at: http://sigport.org/4643.
Reinhard Koch, Robert Bregovic, Atanas Gotchev . (2019). "FAST: Flow-Assisted Shearlet Transform for Densely-sampled Light Field Reconstruction." Web.
1. Reinhard Koch, Robert Bregovic, Atanas Gotchev . FAST: Flow-Assisted Shearlet Transform for Densely-sampled Light Field Reconstruction [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4643

AUDIO FEATURE GENERATION FOR MISSING MODALITY PROBLEM IN VIDEO ACTION RECOGNITION

Hu-Cheng LEE

Despite the recent success of multi-modal action recognition in videos, in reality, we usually confront the situation that some data are not available beforehand, especially for multimodal data. For example, while vision and audio data are required to address the multi-modal action recognition, audio tracks in videos are easily lost due to the broken files or the limitation of devices. To cope with this sound-missing problem, we present an approach to simulating deep audio feature from merely spatial-temporal vision data.

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Authors:
Hu-Cheng LEE
Hu-Cheng Lee, Chih-Yu Lin, Pin-Chun Hsu, Winston H. Hsu
Submitted On:
14 May 2019 - 5:08am
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20190516_AUDIO_FEATURE_GENERATION_FOR_MISSING_MODALITY_PROBLEM_IN_VIDEO_ACTION_RECOGNITION.pptx

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[1] Hu-Cheng Lee, Chih-Yu Lin, Pin-Chun Hsu, Winston H. Hsu, "AUDIO FEATURE GENERATION FOR MISSING MODALITY PROBLEM IN VIDEO ACTION RECOGNITION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4504. Accessed: Sep. 20, 2019.
@article{4504-19,
url = {http://sigport.org/4504},
author = {Hu-Cheng Lee; Chih-Yu Lin; Pin-Chun Hsu; Winston H. Hsu },
publisher = {IEEE SigPort},
title = {AUDIO FEATURE GENERATION FOR MISSING MODALITY PROBLEM IN VIDEO ACTION RECOGNITION},
year = {2019} }
TY - EJOUR
T1 - AUDIO FEATURE GENERATION FOR MISSING MODALITY PROBLEM IN VIDEO ACTION RECOGNITION
AU - Hu-Cheng Lee; Chih-Yu Lin; Pin-Chun Hsu; Winston H. Hsu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4504
ER -
Hu-Cheng Lee, Chih-Yu Lin, Pin-Chun Hsu, Winston H. Hsu. (2019). AUDIO FEATURE GENERATION FOR MISSING MODALITY PROBLEM IN VIDEO ACTION RECOGNITION. IEEE SigPort. http://sigport.org/4504
Hu-Cheng Lee, Chih-Yu Lin, Pin-Chun Hsu, Winston H. Hsu, 2019. AUDIO FEATURE GENERATION FOR MISSING MODALITY PROBLEM IN VIDEO ACTION RECOGNITION. Available at: http://sigport.org/4504.
Hu-Cheng Lee, Chih-Yu Lin, Pin-Chun Hsu, Winston H. Hsu. (2019). "AUDIO FEATURE GENERATION FOR MISSING MODALITY PROBLEM IN VIDEO ACTION RECOGNITION." Web.
1. Hu-Cheng Lee, Chih-Yu Lin, Pin-Chun Hsu, Winston H. Hsu. AUDIO FEATURE GENERATION FOR MISSING MODALITY PROBLEM IN VIDEO ACTION RECOGNITION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4504

Dynamic Temporal Alignment of Speech to Lips

Tavi Halperin
Ariel Ephrat

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Authors:
Tavi Halperin
Ariel Ephrat
Shmuel Peleg
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8 May 2019 - 2:14am
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[1] Shmuel Peleg, "Dynamic Temporal Alignment of Speech to Lips", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4017. Accessed: Sep. 20, 2019.
@article{4017-19,
url = {http://sigport.org/4017},
author = {Shmuel Peleg },
publisher = {IEEE SigPort},
title = {Dynamic Temporal Alignment of Speech to Lips},
year = {2019} }
TY - EJOUR
T1 - Dynamic Temporal Alignment of Speech to Lips
AU - Shmuel Peleg
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4017
ER -
Shmuel Peleg. (2019). Dynamic Temporal Alignment of Speech to Lips. IEEE SigPort. http://sigport.org/4017
Shmuel Peleg, 2019. Dynamic Temporal Alignment of Speech to Lips. Available at: http://sigport.org/4017.
Shmuel Peleg. (2019). "Dynamic Temporal Alignment of Speech to Lips." Web.
1. Shmuel Peleg. Dynamic Temporal Alignment of Speech to Lips [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4017

Learning Shared Vector Representations of Lyrics and Chords in Music

Timothy Greer

Music has a powerful influence on a listener's emotions. In this paper, we represent lyrics and chords in a shared vector space using a phrase-aligned chord-and-lyrics corpus. We show that models that use these shared representations predict a listener's emotion while hearing musical passages better than models that do not use these representations. Additionally, we conduct a visual analysis of these learnt shared vector representations and explain how they support existing theories in music.

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Authors:
Timothy Greer
Timothy Greer, Karan Singla, Benjamin Ma, and Shrikanth Narayanan
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7 May 2019 - 8:12pm
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[1] Timothy Greer, Karan Singla, Benjamin Ma, and Shrikanth Narayanan, "Learning Shared Vector Representations of Lyrics and Chords in Music", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3971. Accessed: Sep. 20, 2019.
@article{3971-19,
url = {http://sigport.org/3971},
author = {Timothy Greer; Karan Singla; Benjamin Ma; and Shrikanth Narayanan },
publisher = {IEEE SigPort},
title = {Learning Shared Vector Representations of Lyrics and Chords in Music},
year = {2019} }
TY - EJOUR
T1 - Learning Shared Vector Representations of Lyrics and Chords in Music
AU - Timothy Greer; Karan Singla; Benjamin Ma; and Shrikanth Narayanan
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3971
ER -
Timothy Greer, Karan Singla, Benjamin Ma, and Shrikanth Narayanan. (2019). Learning Shared Vector Representations of Lyrics and Chords in Music. IEEE SigPort. http://sigport.org/3971
Timothy Greer, Karan Singla, Benjamin Ma, and Shrikanth Narayanan, 2019. Learning Shared Vector Representations of Lyrics and Chords in Music. Available at: http://sigport.org/3971.
Timothy Greer, Karan Singla, Benjamin Ma, and Shrikanth Narayanan. (2019). "Learning Shared Vector Representations of Lyrics and Chords in Music." Web.
1. Timothy Greer, Karan Singla, Benjamin Ma, and Shrikanth Narayanan. Learning Shared Vector Representations of Lyrics and Chords in Music [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3971

Disparity Map Estimation from Cross-modal Stereo

Thapanapong Rukkanchanunt

Mono-modal stereo matching problem has been studied for decades. The introduction of cross-modal stereo systems in industrial scene increases the interest in cross-modal stereo matching. The existing algorithms mostly consider mono-modal setting so they do not translate well in cross-modal setting. Recent development for cross-modal stereo considers small local matching and focus mainly on joint enhancement. Therefore, we propose a guided filter-based stereo matching algorithm. It works by integrating guided filter equation in a basic cost function for cost volume generation.

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Authors:
Thapanapong Rukkanchanunt
Thapanapong Rukkanchanunt, Takashi Shibata, Masayuki Tanaka, Masatoshi Okutomi
Submitted On:
28 November 2018 - 12:15am
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[1] Thapanapong Rukkanchanunt, Takashi Shibata, Masayuki Tanaka, Masatoshi Okutomi, "Disparity Map Estimation from Cross-modal Stereo", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3819. Accessed: Sep. 20, 2019.
@article{3819-18,
url = {http://sigport.org/3819},
author = {Thapanapong Rukkanchanunt; Takashi Shibata; Masayuki Tanaka; Masatoshi Okutomi },
publisher = {IEEE SigPort},
title = {Disparity Map Estimation from Cross-modal Stereo},
year = {2018} }
TY - EJOUR
T1 - Disparity Map Estimation from Cross-modal Stereo
AU - Thapanapong Rukkanchanunt; Takashi Shibata; Masayuki Tanaka; Masatoshi Okutomi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3819
ER -
Thapanapong Rukkanchanunt, Takashi Shibata, Masayuki Tanaka, Masatoshi Okutomi. (2018). Disparity Map Estimation from Cross-modal Stereo. IEEE SigPort. http://sigport.org/3819
Thapanapong Rukkanchanunt, Takashi Shibata, Masayuki Tanaka, Masatoshi Okutomi, 2018. Disparity Map Estimation from Cross-modal Stereo. Available at: http://sigport.org/3819.
Thapanapong Rukkanchanunt, Takashi Shibata, Masayuki Tanaka, Masatoshi Okutomi. (2018). "Disparity Map Estimation from Cross-modal Stereo." Web.
1. Thapanapong Rukkanchanunt, Takashi Shibata, Masayuki Tanaka, Masatoshi Okutomi. Disparity Map Estimation from Cross-modal Stereo [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3819

CNN-BASED ACTION RECOGNITION USING ADAPTIVE MULTISCALE DEPTH MOTION MAPS AND STABLE JOINT DISTANCE MAPS

Junyou He

Human action recognition has a wide range of applications including biometrics and surveillance. Existing methods mostly focus on a single modality, insufficient to characterize variations among different motions. To address this problem, we present a CNN-based human action recognition framework by fusing depth and skeleton modalities. The proposed Adaptive Multiscale Depth Motion Maps (AM-DMMs) are calculated from depth maps to capture shape, motion cues. Moreover, adaptive temporal windows ensure that AM-DMMs are robust to motion speed variations.

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Authors:
Junyou He
Junyou He,Hailun Xia,Chunyan Feng,Yunfei Chu
Submitted On:
20 November 2018 - 5:44am
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CNN-BASED ACTION RECOGNITION USING ADAPTIVE MULTISCALE DEPTH MOTION MAPS AND STABLE JOINT DISTANCE MAPS

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[1] Junyou He,Hailun Xia,Chunyan Feng,Yunfei Chu, "CNN-BASED ACTION RECOGNITION USING ADAPTIVE MULTISCALE DEPTH MOTION MAPS AND STABLE JOINT DISTANCE MAPS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3692. Accessed: Sep. 20, 2019.
@article{3692-18,
url = {http://sigport.org/3692},
author = {Junyou He;Hailun Xia;Chunyan Feng;Yunfei Chu },
publisher = {IEEE SigPort},
title = {CNN-BASED ACTION RECOGNITION USING ADAPTIVE MULTISCALE DEPTH MOTION MAPS AND STABLE JOINT DISTANCE MAPS},
year = {2018} }
TY - EJOUR
T1 - CNN-BASED ACTION RECOGNITION USING ADAPTIVE MULTISCALE DEPTH MOTION MAPS AND STABLE JOINT DISTANCE MAPS
AU - Junyou He;Hailun Xia;Chunyan Feng;Yunfei Chu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3692
ER -
Junyou He,Hailun Xia,Chunyan Feng,Yunfei Chu. (2018). CNN-BASED ACTION RECOGNITION USING ADAPTIVE MULTISCALE DEPTH MOTION MAPS AND STABLE JOINT DISTANCE MAPS. IEEE SigPort. http://sigport.org/3692
Junyou He,Hailun Xia,Chunyan Feng,Yunfei Chu, 2018. CNN-BASED ACTION RECOGNITION USING ADAPTIVE MULTISCALE DEPTH MOTION MAPS AND STABLE JOINT DISTANCE MAPS. Available at: http://sigport.org/3692.
Junyou He,Hailun Xia,Chunyan Feng,Yunfei Chu. (2018). "CNN-BASED ACTION RECOGNITION USING ADAPTIVE MULTISCALE DEPTH MOTION MAPS AND STABLE JOINT DISTANCE MAPS." Web.
1. Junyou He,Hailun Xia,Chunyan Feng,Yunfei Chu. CNN-BASED ACTION RECOGNITION USING ADAPTIVE MULTISCALE DEPTH MOTION MAPS AND STABLE JOINT DISTANCE MAPS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3692

Can DNNs Learn to Lipread Full Sentences ?

George Sterpu

Finding visual features and suitable models for lipreading tasks that are more complex than a well-constrained vocabulary has proven challenging. This paper explores state-of-the-art Deep Neural Network architectures for lipreading based on a Sequence to Sequence Recurrent Neural Network. We report results for both hand-crafted and 2D/3D Convolutional Neural Network visual front-ends, online monotonic attention, and a joint Connectionist Temporal Classification-Sequence-to-Sequence loss.

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Authors:
George Sterpu
George Sterpu, Christian Saam, Naomi Harte
Submitted On:
8 October 2018 - 1:50am
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[1] George Sterpu, Christian Saam, Naomi Harte, "Can DNNs Learn to Lipread Full Sentences ?", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3608. Accessed: Sep. 20, 2019.
@article{3608-18,
url = {http://sigport.org/3608},
author = {George Sterpu; Christian Saam; Naomi Harte },
publisher = {IEEE SigPort},
title = {Can DNNs Learn to Lipread Full Sentences ?},
year = {2018} }
TY - EJOUR
T1 - Can DNNs Learn to Lipread Full Sentences ?
AU - George Sterpu; Christian Saam; Naomi Harte
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3608
ER -
George Sterpu, Christian Saam, Naomi Harte. (2018). Can DNNs Learn to Lipread Full Sentences ?. IEEE SigPort. http://sigport.org/3608
George Sterpu, Christian Saam, Naomi Harte, 2018. Can DNNs Learn to Lipread Full Sentences ?. Available at: http://sigport.org/3608.
George Sterpu, Christian Saam, Naomi Harte. (2018). "Can DNNs Learn to Lipread Full Sentences ?." Web.
1. George Sterpu, Christian Saam, Naomi Harte. Can DNNs Learn to Lipread Full Sentences ? [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3608

ICIP poster presentation Paper TP.P5.2 (1865): 'SHOT SCALE ANALYSIS IN MOVIES BY CONVOLUTIONAL NEURAL NETWORKS'

Sergio Benini

The apparent distance of the camera from the subject of a filmed scene, namely shot scale, is one of the prominent formal features of any filmic product, endowed with both stylistic and narrative functions. In this work we propose to use Convolutional Neural Networks for the automatic classification of shot scale into Close-, Medium-, or Long-shots.

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Authors:
Sergio Benini
Mattia Savardi, Alberto Signoroni, Pierangelo Migliorati, and Sergio Benini
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5 October 2018 - 5:02am
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[1] Mattia Savardi, Alberto Signoroni, Pierangelo Migliorati, and Sergio Benini, "ICIP poster presentation Paper TP.P5.2 (1865): 'SHOT SCALE ANALYSIS IN MOVIES BY CONVOLUTIONAL NEURAL NETWORKS'", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3523. Accessed: Sep. 20, 2019.
@article{3523-18,
url = {http://sigport.org/3523},
author = {Mattia Savardi; Alberto Signoroni; Pierangelo Migliorati; and Sergio Benini },
publisher = {IEEE SigPort},
title = {ICIP poster presentation Paper TP.P5.2 (1865): 'SHOT SCALE ANALYSIS IN MOVIES BY CONVOLUTIONAL NEURAL NETWORKS'},
year = {2018} }
TY - EJOUR
T1 - ICIP poster presentation Paper TP.P5.2 (1865): 'SHOT SCALE ANALYSIS IN MOVIES BY CONVOLUTIONAL NEURAL NETWORKS'
AU - Mattia Savardi; Alberto Signoroni; Pierangelo Migliorati; and Sergio Benini
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3523
ER -
Mattia Savardi, Alberto Signoroni, Pierangelo Migliorati, and Sergio Benini. (2018). ICIP poster presentation Paper TP.P5.2 (1865): 'SHOT SCALE ANALYSIS IN MOVIES BY CONVOLUTIONAL NEURAL NETWORKS'. IEEE SigPort. http://sigport.org/3523
Mattia Savardi, Alberto Signoroni, Pierangelo Migliorati, and Sergio Benini, 2018. ICIP poster presentation Paper TP.P5.2 (1865): 'SHOT SCALE ANALYSIS IN MOVIES BY CONVOLUTIONAL NEURAL NETWORKS'. Available at: http://sigport.org/3523.
Mattia Savardi, Alberto Signoroni, Pierangelo Migliorati, and Sergio Benini. (2018). "ICIP poster presentation Paper TP.P5.2 (1865): 'SHOT SCALE ANALYSIS IN MOVIES BY CONVOLUTIONAL NEURAL NETWORKS'." Web.
1. Mattia Savardi, Alberto Signoroni, Pierangelo Migliorati, and Sergio Benini. ICIP poster presentation Paper TP.P5.2 (1865): 'SHOT SCALE ANALYSIS IN MOVIES BY CONVOLUTIONAL NEURAL NETWORKS' [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3523

WATCH, LISTEN ONCE, AND SYNC: AUDIO-VISUAL SYNCHRONIZATION WITH MULTI-MODAL REGRESSION CNN

Toshiki Kikuchi

Recovering audio-visual synchronization is an important task in the field of visual speech processing.

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Authors:
Toshiki Kikuchi
Toshiki Kikuchi, Yuko Ozasa
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13 April 2018 - 12:19am
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[1] Toshiki Kikuchi, Yuko Ozasa, "WATCH, LISTEN ONCE, AND SYNC: AUDIO-VISUAL SYNCHRONIZATION WITH MULTI-MODAL REGRESSION CNN", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2585. Accessed: Sep. 20, 2019.
@article{2585-18,
url = {http://sigport.org/2585},
author = {Toshiki Kikuchi; Yuko Ozasa },
publisher = {IEEE SigPort},
title = {WATCH, LISTEN ONCE, AND SYNC: AUDIO-VISUAL SYNCHRONIZATION WITH MULTI-MODAL REGRESSION CNN},
year = {2018} }
TY - EJOUR
T1 - WATCH, LISTEN ONCE, AND SYNC: AUDIO-VISUAL SYNCHRONIZATION WITH MULTI-MODAL REGRESSION CNN
AU - Toshiki Kikuchi; Yuko Ozasa
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2585
ER -
Toshiki Kikuchi, Yuko Ozasa. (2018). WATCH, LISTEN ONCE, AND SYNC: AUDIO-VISUAL SYNCHRONIZATION WITH MULTI-MODAL REGRESSION CNN. IEEE SigPort. http://sigport.org/2585
Toshiki Kikuchi, Yuko Ozasa, 2018. WATCH, LISTEN ONCE, AND SYNC: AUDIO-VISUAL SYNCHRONIZATION WITH MULTI-MODAL REGRESSION CNN. Available at: http://sigport.org/2585.
Toshiki Kikuchi, Yuko Ozasa. (2018). "WATCH, LISTEN ONCE, AND SYNC: AUDIO-VISUAL SYNCHRONIZATION WITH MULTI-MODAL REGRESSION CNN." Web.
1. Toshiki Kikuchi, Yuko Ozasa. WATCH, LISTEN ONCE, AND SYNC: AUDIO-VISUAL SYNCHRONIZATION WITH MULTI-MODAL REGRESSION CNN [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2585

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