Sorry, you need to enable JavaScript to visit this website.

Image/Video Processing

A MULTI-PERSPECTIVE APPROACH TO ANOMALY DETECTION FOR SELF-AWARE EMBODIED AGENTS


This paper focuses on multi-sensor anomaly detection for moving cognitive agents using both external and private first-person visual observations. Both observation types are used to characterize agents’ motion in a given environment. The proposed method generates locally uniform motion models by dividing a Gaussian process that approximates agents’ displacements on the scene and provides a Shared Level (SL) self-awareness based on Environment Centered (EC) models.

Paper Details

Authors:
Mohamad Baydoun, Mahdyar Ravanbakhsh, Damian Campo, Pablo Marin, David Martin, Lucio Marcenaro, Andrea Cavallaro, Carlo S. Regazzoni
Submitted On:
18 April 2018 - 10:40am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

SS-L2.5 A MULTI-PERSPECTIVE APPROACH TO ANOMALY DETECTION FOR SELF-AWARE EMBODIED AGENTS.pdf

(96 downloads)

Subscribe

[1] Mohamad Baydoun, Mahdyar Ravanbakhsh, Damian Campo, Pablo Marin, David Martin, Lucio Marcenaro, Andrea Cavallaro, Carlo S. Regazzoni, "A MULTI-PERSPECTIVE APPROACH TO ANOMALY DETECTION FOR SELF-AWARE EMBODIED AGENTS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2964. Accessed: Jan. 16, 2019.
@article{2964-18,
url = {http://sigport.org/2964},
author = {Mohamad Baydoun; Mahdyar Ravanbakhsh; Damian Campo; Pablo Marin; David Martin; Lucio Marcenaro; Andrea Cavallaro; Carlo S. Regazzoni },
publisher = {IEEE SigPort},
title = {A MULTI-PERSPECTIVE APPROACH TO ANOMALY DETECTION FOR SELF-AWARE EMBODIED AGENTS},
year = {2018} }
TY - EJOUR
T1 - A MULTI-PERSPECTIVE APPROACH TO ANOMALY DETECTION FOR SELF-AWARE EMBODIED AGENTS
AU - Mohamad Baydoun; Mahdyar Ravanbakhsh; Damian Campo; Pablo Marin; David Martin; Lucio Marcenaro; Andrea Cavallaro; Carlo S. Regazzoni
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2964
ER -
Mohamad Baydoun, Mahdyar Ravanbakhsh, Damian Campo, Pablo Marin, David Martin, Lucio Marcenaro, Andrea Cavallaro, Carlo S. Regazzoni. (2018). A MULTI-PERSPECTIVE APPROACH TO ANOMALY DETECTION FOR SELF-AWARE EMBODIED AGENTS. IEEE SigPort. http://sigport.org/2964
Mohamad Baydoun, Mahdyar Ravanbakhsh, Damian Campo, Pablo Marin, David Martin, Lucio Marcenaro, Andrea Cavallaro, Carlo S. Regazzoni, 2018. A MULTI-PERSPECTIVE APPROACH TO ANOMALY DETECTION FOR SELF-AWARE EMBODIED AGENTS. Available at: http://sigport.org/2964.
Mohamad Baydoun, Mahdyar Ravanbakhsh, Damian Campo, Pablo Marin, David Martin, Lucio Marcenaro, Andrea Cavallaro, Carlo S. Regazzoni. (2018). "A MULTI-PERSPECTIVE APPROACH TO ANOMALY DETECTION FOR SELF-AWARE EMBODIED AGENTS." Web.
1. Mohamad Baydoun, Mahdyar Ravanbakhsh, Damian Campo, Pablo Marin, David Martin, Lucio Marcenaro, Andrea Cavallaro, Carlo S. Regazzoni. A MULTI-PERSPECTIVE APPROACH TO ANOMALY DETECTION FOR SELF-AWARE EMBODIED AGENTS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2964

A Low Power Hardware Implementation of Multi-Object DPM Detector for Autonomous Driving


Object detection is a fundamental process in traffic management systems and self-driving cars. Deformable part model (DPM) is a popular and competitive detector for its high precision. This paper presents a programmable, low power hardware implementation of DPM based object detection for real-time applications.

Paper Details

Authors:
Oladiran G. Olaleye, Bappaditya Dey, Kasem Khalil, Magdy A. Bayoumi
Submitted On:
14 April 2018 - 11:50pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Alaa-Poster-36Wx39H.pdf

(86 downloads)

Subscribe

[1] Oladiran G. Olaleye, Bappaditya Dey, Kasem Khalil, Magdy A. Bayoumi, "A Low Power Hardware Implementation of Multi-Object DPM Detector for Autonomous Driving", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2873. Accessed: Jan. 16, 2019.
@article{2873-18,
url = {http://sigport.org/2873},
author = {Oladiran G. Olaleye; Bappaditya Dey; Kasem Khalil; Magdy A. Bayoumi },
publisher = {IEEE SigPort},
title = {A Low Power Hardware Implementation of Multi-Object DPM Detector for Autonomous Driving},
year = {2018} }
TY - EJOUR
T1 - A Low Power Hardware Implementation of Multi-Object DPM Detector for Autonomous Driving
AU - Oladiran G. Olaleye; Bappaditya Dey; Kasem Khalil; Magdy A. Bayoumi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2873
ER -
Oladiran G. Olaleye, Bappaditya Dey, Kasem Khalil, Magdy A. Bayoumi. (2018). A Low Power Hardware Implementation of Multi-Object DPM Detector for Autonomous Driving. IEEE SigPort. http://sigport.org/2873
Oladiran G. Olaleye, Bappaditya Dey, Kasem Khalil, Magdy A. Bayoumi, 2018. A Low Power Hardware Implementation of Multi-Object DPM Detector for Autonomous Driving. Available at: http://sigport.org/2873.
Oladiran G. Olaleye, Bappaditya Dey, Kasem Khalil, Magdy A. Bayoumi. (2018). "A Low Power Hardware Implementation of Multi-Object DPM Detector for Autonomous Driving." Web.
1. Oladiran G. Olaleye, Bappaditya Dey, Kasem Khalil, Magdy A. Bayoumi. A Low Power Hardware Implementation of Multi-Object DPM Detector for Autonomous Driving [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2873

Guided Image Filtering with Arbitrary Window Function


In this paper, we propose an extension of guided image filtering to support arbitrary window functions. The guided image filtering is a fast edge-preserving filter based on a local linearity assumption. The filter supports not only image smoothing but also edge enhancement and image interpolation. The guided image filter assumes that an input image is a local linear transformation of a guidance image, and the assumption is supported in a local finite region. For realizing the supposition, the guided image filtering consists of a stack of box filtering.

Paper Details

Authors:
Norishige Fukushima, Kenjiro Sugimoto, Sei-ichiro Kamata
Submitted On:
14 April 2018 - 5:57pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

icassp_poster_fukushima.pdf

(119 downloads)

Subscribe

[1] Norishige Fukushima, Kenjiro Sugimoto, Sei-ichiro Kamata, "Guided Image Filtering with Arbitrary Window Function", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2857. Accessed: Jan. 16, 2019.
@article{2857-18,
url = {http://sigport.org/2857},
author = {Norishige Fukushima; Kenjiro Sugimoto; Sei-ichiro Kamata },
publisher = {IEEE SigPort},
title = {Guided Image Filtering with Arbitrary Window Function},
year = {2018} }
TY - EJOUR
T1 - Guided Image Filtering with Arbitrary Window Function
AU - Norishige Fukushima; Kenjiro Sugimoto; Sei-ichiro Kamata
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2857
ER -
Norishige Fukushima, Kenjiro Sugimoto, Sei-ichiro Kamata. (2018). Guided Image Filtering with Arbitrary Window Function. IEEE SigPort. http://sigport.org/2857
Norishige Fukushima, Kenjiro Sugimoto, Sei-ichiro Kamata, 2018. Guided Image Filtering with Arbitrary Window Function. Available at: http://sigport.org/2857.
Norishige Fukushima, Kenjiro Sugimoto, Sei-ichiro Kamata. (2018). "Guided Image Filtering with Arbitrary Window Function." Web.
1. Norishige Fukushima, Kenjiro Sugimoto, Sei-ichiro Kamata. Guided Image Filtering with Arbitrary Window Function [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2857

MODEL BASED DEEP LEARNING IN FREE BREATHING, UNGATED, CARDIAC MRI RECOVERY


We introduce a model-based reconstruction
framework with deep learned (DL) and smoothness regularization
on manifolds (STORM) priors to recover free
breathing and ungated (FBU) cardiac MRI from highly undersampled
measurements. The DL priors enable us to exploit
the local correlations, while the STORM prior enables
us to make use of the extensive non-local similarities that are
subject dependent. We introduce a novel model-based formulation
that allows the seamless integration of deep learning

Paper Details

Authors:
Sampurna Biswas, Hemant K. Aggarwal, Sunrita Poddar, Mathews Jacob
Submitted On:
14 April 2018 - 1:52pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

icassp_poster_final.pptx

(94 downloads)

Subscribe

[1] Sampurna Biswas, Hemant K. Aggarwal, Sunrita Poddar, Mathews Jacob, "MODEL BASED DEEP LEARNING IN FREE BREATHING, UNGATED, CARDIAC MRI RECOVERY ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2845. Accessed: Jan. 16, 2019.
@article{2845-18,
url = {http://sigport.org/2845},
author = {Sampurna Biswas; Hemant K. Aggarwal; Sunrita Poddar; Mathews Jacob },
publisher = {IEEE SigPort},
title = {MODEL BASED DEEP LEARNING IN FREE BREATHING, UNGATED, CARDIAC MRI RECOVERY },
year = {2018} }
TY - EJOUR
T1 - MODEL BASED DEEP LEARNING IN FREE BREATHING, UNGATED, CARDIAC MRI RECOVERY
AU - Sampurna Biswas; Hemant K. Aggarwal; Sunrita Poddar; Mathews Jacob
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2845
ER -
Sampurna Biswas, Hemant K. Aggarwal, Sunrita Poddar, Mathews Jacob. (2018). MODEL BASED DEEP LEARNING IN FREE BREATHING, UNGATED, CARDIAC MRI RECOVERY . IEEE SigPort. http://sigport.org/2845
Sampurna Biswas, Hemant K. Aggarwal, Sunrita Poddar, Mathews Jacob, 2018. MODEL BASED DEEP LEARNING IN FREE BREATHING, UNGATED, CARDIAC MRI RECOVERY . Available at: http://sigport.org/2845.
Sampurna Biswas, Hemant K. Aggarwal, Sunrita Poddar, Mathews Jacob. (2018). "MODEL BASED DEEP LEARNING IN FREE BREATHING, UNGATED, CARDIAC MRI RECOVERY ." Web.
1. Sampurna Biswas, Hemant K. Aggarwal, Sunrita Poddar, Mathews Jacob. MODEL BASED DEEP LEARNING IN FREE BREATHING, UNGATED, CARDIAC MRI RECOVERY [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2845

INVESTIGATION IN SPATIAL-TEMPORAL DOMAIN FOR FACE SPOOF DETECTION


This paper focuses on face spoofing detection using video. The purpose is to find out the best scheme for this task in the end-to-end learning manner. We investigate 4 different types of structure to fully exploit the raw data in its spatial-temporal domain, which are the pure CNN, CNN with 3D convolu-tion, CNN+LSTM and CNN+Conv-LSTM. Moreover, anoth-er stream built on optical flow is also used, and with a proper fusion method, it can improve the accuracy. In experiments, we compare schemes on the raw data in single stream and fusion methods with optical flow in two streams.

Paper Details

Authors:
Zhonglin Sun, Li Sun, Qingli Li
Submitted On:
14 April 2018 - 1:35pm
Short Link:
Type:
Event:
Paper Code:
Document Year:
Cite

Document Files

icassp_template.pdf

(161 downloads)

Subscribe

[1] Zhonglin Sun, Li Sun, Qingli Li, "INVESTIGATION IN SPATIAL-TEMPORAL DOMAIN FOR FACE SPOOF DETECTION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2844. Accessed: Jan. 16, 2019.
@article{2844-18,
url = {http://sigport.org/2844},
author = {Zhonglin Sun; Li Sun; Qingli Li },
publisher = {IEEE SigPort},
title = {INVESTIGATION IN SPATIAL-TEMPORAL DOMAIN FOR FACE SPOOF DETECTION},
year = {2018} }
TY - EJOUR
T1 - INVESTIGATION IN SPATIAL-TEMPORAL DOMAIN FOR FACE SPOOF DETECTION
AU - Zhonglin Sun; Li Sun; Qingli Li
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2844
ER -
Zhonglin Sun, Li Sun, Qingli Li. (2018). INVESTIGATION IN SPATIAL-TEMPORAL DOMAIN FOR FACE SPOOF DETECTION. IEEE SigPort. http://sigport.org/2844
Zhonglin Sun, Li Sun, Qingli Li, 2018. INVESTIGATION IN SPATIAL-TEMPORAL DOMAIN FOR FACE SPOOF DETECTION. Available at: http://sigport.org/2844.
Zhonglin Sun, Li Sun, Qingli Li. (2018). "INVESTIGATION IN SPATIAL-TEMPORAL DOMAIN FOR FACE SPOOF DETECTION." Web.
1. Zhonglin Sun, Li Sun, Qingli Li. INVESTIGATION IN SPATIAL-TEMPORAL DOMAIN FOR FACE SPOOF DETECTION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2844

FACIAL FEATURE-INTEGRATED INTER-CAMERA HUMAN TRACKING

Paper Details

Authors:
Jenq-Neng Hwang
Submitted On:
14 April 2018 - 9:58am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

poster_2018.pdf

(117 downloads)

Subscribe

[1] Jenq-Neng Hwang, "FACIAL FEATURE-INTEGRATED INTER-CAMERA HUMAN TRACKING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2832. Accessed: Jan. 16, 2019.
@article{2832-18,
url = {http://sigport.org/2832},
author = {Jenq-Neng Hwang },
publisher = {IEEE SigPort},
title = {FACIAL FEATURE-INTEGRATED INTER-CAMERA HUMAN TRACKING},
year = {2018} }
TY - EJOUR
T1 - FACIAL FEATURE-INTEGRATED INTER-CAMERA HUMAN TRACKING
AU - Jenq-Neng Hwang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2832
ER -
Jenq-Neng Hwang. (2018). FACIAL FEATURE-INTEGRATED INTER-CAMERA HUMAN TRACKING. IEEE SigPort. http://sigport.org/2832
Jenq-Neng Hwang, 2018. FACIAL FEATURE-INTEGRATED INTER-CAMERA HUMAN TRACKING. Available at: http://sigport.org/2832.
Jenq-Neng Hwang. (2018). "FACIAL FEATURE-INTEGRATED INTER-CAMERA HUMAN TRACKING." Web.
1. Jenq-Neng Hwang. FACIAL FEATURE-INTEGRATED INTER-CAMERA HUMAN TRACKING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2832

Depth Super-resolution with Deep Edge-inference Network and Edge-guided Depth Filling

Paper Details

Authors:
Submitted On:
14 April 2018 - 8:38am
Short Link:
Type:
Event:

Document Files

Depth Super-resolution

(121 downloads)

Subscribe

[1] , "Depth Super-resolution with Deep Edge-inference Network and Edge-guided Depth Filling", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2826. Accessed: Jan. 16, 2019.
@article{2826-18,
url = {http://sigport.org/2826},
author = { },
publisher = {IEEE SigPort},
title = {Depth Super-resolution with Deep Edge-inference Network and Edge-guided Depth Filling},
year = {2018} }
TY - EJOUR
T1 - Depth Super-resolution with Deep Edge-inference Network and Edge-guided Depth Filling
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2826
ER -
. (2018). Depth Super-resolution with Deep Edge-inference Network and Edge-guided Depth Filling. IEEE SigPort. http://sigport.org/2826
, 2018. Depth Super-resolution with Deep Edge-inference Network and Edge-guided Depth Filling. Available at: http://sigport.org/2826.
. (2018). "Depth Super-resolution with Deep Edge-inference Network and Edge-guided Depth Filling." Web.
1. . Depth Super-resolution with Deep Edge-inference Network and Edge-guided Depth Filling [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2826

Unbiased Distance based Non-local Fuzzy Means

Paper Details

Authors:
Xiaoyao Li, Yicong Zhou, Jing Zhang, Lianhong Wang
Submitted On:
14 April 2018 - 8:00am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

UDNLFM

(101 downloads)

Subscribe

[1] Xiaoyao Li, Yicong Zhou, Jing Zhang, Lianhong Wang, "Unbiased Distance based Non-local Fuzzy Means", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2822. Accessed: Jan. 16, 2019.
@article{2822-18,
url = {http://sigport.org/2822},
author = {Xiaoyao Li; Yicong Zhou; Jing Zhang; Lianhong Wang },
publisher = {IEEE SigPort},
title = {Unbiased Distance based Non-local Fuzzy Means},
year = {2018} }
TY - EJOUR
T1 - Unbiased Distance based Non-local Fuzzy Means
AU - Xiaoyao Li; Yicong Zhou; Jing Zhang; Lianhong Wang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2822
ER -
Xiaoyao Li, Yicong Zhou, Jing Zhang, Lianhong Wang. (2018). Unbiased Distance based Non-local Fuzzy Means. IEEE SigPort. http://sigport.org/2822
Xiaoyao Li, Yicong Zhou, Jing Zhang, Lianhong Wang, 2018. Unbiased Distance based Non-local Fuzzy Means. Available at: http://sigport.org/2822.
Xiaoyao Li, Yicong Zhou, Jing Zhang, Lianhong Wang. (2018). "Unbiased Distance based Non-local Fuzzy Means." Web.
1. Xiaoyao Li, Yicong Zhou, Jing Zhang, Lianhong Wang. Unbiased Distance based Non-local Fuzzy Means [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2822

UNIVERSAL APPROACH FOR DCT-BASED CONSTANT-TIME GAUSSIAN FILTER WITH MOMENT PRESERVATION

Paper Details

Authors:
Kenjiro Sugimoto, Seisuke Kyochi, Sei-ichiro Kamata
Submitted On:
14 April 2018 - 7:56am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

2018-04-14 ICASSP Poster.pdf

(106 downloads)

Subscribe

[1] Kenjiro Sugimoto, Seisuke Kyochi, Sei-ichiro Kamata, "UNIVERSAL APPROACH FOR DCT-BASED CONSTANT-TIME GAUSSIAN FILTER WITH MOMENT PRESERVATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2821. Accessed: Jan. 16, 2019.
@article{2821-18,
url = {http://sigport.org/2821},
author = {Kenjiro Sugimoto; Seisuke Kyochi; Sei-ichiro Kamata },
publisher = {IEEE SigPort},
title = {UNIVERSAL APPROACH FOR DCT-BASED CONSTANT-TIME GAUSSIAN FILTER WITH MOMENT PRESERVATION},
year = {2018} }
TY - EJOUR
T1 - UNIVERSAL APPROACH FOR DCT-BASED CONSTANT-TIME GAUSSIAN FILTER WITH MOMENT PRESERVATION
AU - Kenjiro Sugimoto; Seisuke Kyochi; Sei-ichiro Kamata
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2821
ER -
Kenjiro Sugimoto, Seisuke Kyochi, Sei-ichiro Kamata. (2018). UNIVERSAL APPROACH FOR DCT-BASED CONSTANT-TIME GAUSSIAN FILTER WITH MOMENT PRESERVATION. IEEE SigPort. http://sigport.org/2821
Kenjiro Sugimoto, Seisuke Kyochi, Sei-ichiro Kamata, 2018. UNIVERSAL APPROACH FOR DCT-BASED CONSTANT-TIME GAUSSIAN FILTER WITH MOMENT PRESERVATION. Available at: http://sigport.org/2821.
Kenjiro Sugimoto, Seisuke Kyochi, Sei-ichiro Kamata. (2018). "UNIVERSAL APPROACH FOR DCT-BASED CONSTANT-TIME GAUSSIAN FILTER WITH MOMENT PRESERVATION." Web.
1. Kenjiro Sugimoto, Seisuke Kyochi, Sei-ichiro Kamata. UNIVERSAL APPROACH FOR DCT-BASED CONSTANT-TIME GAUSSIAN FILTER WITH MOMENT PRESERVATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2821

Fast Vehicle Detection with Lateral Convolutional Neural Network


Fast Vehicle Detection with Lateral Convolutional Neural Network

Paper Details

Authors:
Chen-Hang HE, Kin-Man LAM
Submitted On:
17 April 2018 - 8:58am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Lateral-CNN

(123 downloads)

Lateral-CNN Slides.pptx

(130 downloads)

Keywords

Additional Categories

Subscribe

[1] Chen-Hang HE, Kin-Man LAM, "Fast Vehicle Detection with Lateral Convolutional Neural Network", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2808. Accessed: Jan. 16, 2019.
@article{2808-18,
url = {http://sigport.org/2808},
author = {Chen-Hang HE; Kin-Man LAM },
publisher = {IEEE SigPort},
title = {Fast Vehicle Detection with Lateral Convolutional Neural Network},
year = {2018} }
TY - EJOUR
T1 - Fast Vehicle Detection with Lateral Convolutional Neural Network
AU - Chen-Hang HE; Kin-Man LAM
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2808
ER -
Chen-Hang HE, Kin-Man LAM. (2018). Fast Vehicle Detection with Lateral Convolutional Neural Network. IEEE SigPort. http://sigport.org/2808
Chen-Hang HE, Kin-Man LAM, 2018. Fast Vehicle Detection with Lateral Convolutional Neural Network. Available at: http://sigport.org/2808.
Chen-Hang HE, Kin-Man LAM. (2018). "Fast Vehicle Detection with Lateral Convolutional Neural Network." Web.
1. Chen-Hang HE, Kin-Man LAM. Fast Vehicle Detection with Lateral Convolutional Neural Network [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2808

Pages