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Image/Video Processing

NEAR INFRARED IMAGERY COLORIZATION


This paper proposes a stacked conditional Generative Adversarial Network-based method for Near InfraRed (NIR) imagery colorization. We propose a variant architecture of Generative Adversarial Network (GAN) that uses multiple loss functions over a conditional probabilistic generative model. We show that this new architecture/loss-function yields better generalization and representation of the generated colored IR images. The proposed approach is evaluated on a large test dataset and compared to recent state of the art methods using standard metrics.

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Authors:
Patricia L. Suarez,Angel D. Sappa,Boris Vintimilla,Riad I. Hammoud
Submitted On:
4 October 2018 - 7:21pm
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NEAR INFRARED IMAGERY COLORIZATION.pdf

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[1] Patricia L. Suarez,Angel D. Sappa,Boris Vintimilla,Riad I. Hammoud, "NEAR INFRARED IMAGERY COLORIZATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3469. Accessed: Mar. 20, 2019.
@article{3469-18,
url = {http://sigport.org/3469},
author = {Patricia L. Suarez;Angel D. Sappa;Boris Vintimilla;Riad I. Hammoud },
publisher = {IEEE SigPort},
title = {NEAR INFRARED IMAGERY COLORIZATION},
year = {2018} }
TY - EJOUR
T1 - NEAR INFRARED IMAGERY COLORIZATION
AU - Patricia L. Suarez;Angel D. Sappa;Boris Vintimilla;Riad I. Hammoud
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3469
ER -
Patricia L. Suarez,Angel D. Sappa,Boris Vintimilla,Riad I. Hammoud. (2018). NEAR INFRARED IMAGERY COLORIZATION. IEEE SigPort. http://sigport.org/3469
Patricia L. Suarez,Angel D. Sappa,Boris Vintimilla,Riad I. Hammoud, 2018. NEAR INFRARED IMAGERY COLORIZATION. Available at: http://sigport.org/3469.
Patricia L. Suarez,Angel D. Sappa,Boris Vintimilla,Riad I. Hammoud. (2018). "NEAR INFRARED IMAGERY COLORIZATION." Web.
1. Patricia L. Suarez,Angel D. Sappa,Boris Vintimilla,Riad I. Hammoud. NEAR INFRARED IMAGERY COLORIZATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3469

Reduction of Poisson noise in coded exposure photography


Coded exposure photography (CEP), originally proposed by Raskar et al., has been known as one of the promising techniques for motion deblurring. In this area, much efforts have been made for designing a fluttered shutter sequence to shape the spectrum of a uniformly motion-blurred image into an invertible one. Since the duty cycle of the fluttered shutters proposed thus far is generally low, the number of photons entering into an image sensor is reduced, which leads to a large Poisson noise in a low lighting condition.

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Authors:
Toshiyuki Yoshida
Submitted On:
4 October 2018 - 7:03pm
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p3.pdf

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[1] Toshiyuki Yoshida, "Reduction of Poisson noise in coded exposure photography", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3468. Accessed: Mar. 20, 2019.
@article{3468-18,
url = {http://sigport.org/3468},
author = {Toshiyuki Yoshida },
publisher = {IEEE SigPort},
title = {Reduction of Poisson noise in coded exposure photography},
year = {2018} }
TY - EJOUR
T1 - Reduction of Poisson noise in coded exposure photography
AU - Toshiyuki Yoshida
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3468
ER -
Toshiyuki Yoshida. (2018). Reduction of Poisson noise in coded exposure photography. IEEE SigPort. http://sigport.org/3468
Toshiyuki Yoshida, 2018. Reduction of Poisson noise in coded exposure photography. Available at: http://sigport.org/3468.
Toshiyuki Yoshida. (2018). "Reduction of Poisson noise in coded exposure photography." Web.
1. Toshiyuki Yoshida. Reduction of Poisson noise in coded exposure photography [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3468

A CONNECTED-TUBE MPP MODEL FOR OBJECT DETECTION WITH APPLICATION TO MATERIALS AND REMOTELY-SENSED IMAGES


• A connected-tube model based on a Marked Point Process (MPP) for strip feature extraction in images is proposed
• A mixed MPP model can be formed by combing the proposed model with other geometric models
• The proposed model can be applied to complex detection tasks, such as short and long fiber detection in material images, road and roof detection in satellite images.

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Authors:
Tianyu Li, Mary Comer, Josiane Zerubia
Submitted On:
4 October 2018 - 3:41pm
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A CONNECTED-TUBE MPP MODEL _Poster.pdf

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[1] Tianyu Li, Mary Comer, Josiane Zerubia, "A CONNECTED-TUBE MPP MODEL FOR OBJECT DETECTION WITH APPLICATION TO MATERIALS AND REMOTELY-SENSED IMAGES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3461. Accessed: Mar. 20, 2019.
@article{3461-18,
url = {http://sigport.org/3461},
author = {Tianyu Li; Mary Comer; Josiane Zerubia },
publisher = {IEEE SigPort},
title = {A CONNECTED-TUBE MPP MODEL FOR OBJECT DETECTION WITH APPLICATION TO MATERIALS AND REMOTELY-SENSED IMAGES},
year = {2018} }
TY - EJOUR
T1 - A CONNECTED-TUBE MPP MODEL FOR OBJECT DETECTION WITH APPLICATION TO MATERIALS AND REMOTELY-SENSED IMAGES
AU - Tianyu Li; Mary Comer; Josiane Zerubia
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3461
ER -
Tianyu Li, Mary Comer, Josiane Zerubia. (2018). A CONNECTED-TUBE MPP MODEL FOR OBJECT DETECTION WITH APPLICATION TO MATERIALS AND REMOTELY-SENSED IMAGES. IEEE SigPort. http://sigport.org/3461
Tianyu Li, Mary Comer, Josiane Zerubia, 2018. A CONNECTED-TUBE MPP MODEL FOR OBJECT DETECTION WITH APPLICATION TO MATERIALS AND REMOTELY-SENSED IMAGES. Available at: http://sigport.org/3461.
Tianyu Li, Mary Comer, Josiane Zerubia. (2018). "A CONNECTED-TUBE MPP MODEL FOR OBJECT DETECTION WITH APPLICATION TO MATERIALS AND REMOTELY-SENSED IMAGES." Web.
1. Tianyu Li, Mary Comer, Josiane Zerubia. A CONNECTED-TUBE MPP MODEL FOR OBJECT DETECTION WITH APPLICATION TO MATERIALS AND REMOTELY-SENSED IMAGES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3461

100+ Times Faster Video Completion by Optical-Flow-Guided Variational Refinement


Despite the higher video-completion quality that recently proposed methods have enabled for a wide variety of cases, their computational complexity remains a major concern. These methods typically frame video completion as an optimization problem over the whole spatiotemporal domain—a problem that is expensive to solve both in time and space. In this paper we propose a lighter-weight multipass video-completion pipeline that replaces global spatiotemporal optimization with simpler frame-by-frame motion reconstruction and refinement.

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Authors:
Alexander Bokov, Dmitriy Vatolin
Submitted On:
4 October 2018 - 2:42pm
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ICIP_v2.pdf

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[1] Alexander Bokov, Dmitriy Vatolin, "100+ Times Faster Video Completion by Optical-Flow-Guided Variational Refinement", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3460. Accessed: Mar. 20, 2019.
@article{3460-18,
url = {http://sigport.org/3460},
author = {Alexander Bokov; Dmitriy Vatolin },
publisher = {IEEE SigPort},
title = {100+ Times Faster Video Completion by Optical-Flow-Guided Variational Refinement},
year = {2018} }
TY - EJOUR
T1 - 100+ Times Faster Video Completion by Optical-Flow-Guided Variational Refinement
AU - Alexander Bokov; Dmitriy Vatolin
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3460
ER -
Alexander Bokov, Dmitriy Vatolin. (2018). 100+ Times Faster Video Completion by Optical-Flow-Guided Variational Refinement. IEEE SigPort. http://sigport.org/3460
Alexander Bokov, Dmitriy Vatolin, 2018. 100+ Times Faster Video Completion by Optical-Flow-Guided Variational Refinement. Available at: http://sigport.org/3460.
Alexander Bokov, Dmitriy Vatolin. (2018). "100+ Times Faster Video Completion by Optical-Flow-Guided Variational Refinement." Web.
1. Alexander Bokov, Dmitriy Vatolin. 100+ Times Faster Video Completion by Optical-Flow-Guided Variational Refinement [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3460

EXAMPLE-BASED SUPER-RESOLUTION FOR POINT-CLOUD VIDEO

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Authors:
Diogo C. Garcia, Tiago A. Fonseca, Ricardo L. de Queiroz
Submitted On:
4 October 2018 - 1:42pm
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Poster_ICIP_2018_SR_Diogo_Tiago.pdf

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[1] Diogo C. Garcia, Tiago A. Fonseca, Ricardo L. de Queiroz, "EXAMPLE-BASED SUPER-RESOLUTION FOR POINT-CLOUD VIDEO", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3459. Accessed: Mar. 20, 2019.
@article{3459-18,
url = {http://sigport.org/3459},
author = {Diogo C. Garcia; Tiago A. Fonseca; Ricardo L. de Queiroz },
publisher = {IEEE SigPort},
title = {EXAMPLE-BASED SUPER-RESOLUTION FOR POINT-CLOUD VIDEO},
year = {2018} }
TY - EJOUR
T1 - EXAMPLE-BASED SUPER-RESOLUTION FOR POINT-CLOUD VIDEO
AU - Diogo C. Garcia; Tiago A. Fonseca; Ricardo L. de Queiroz
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3459
ER -
Diogo C. Garcia, Tiago A. Fonseca, Ricardo L. de Queiroz. (2018). EXAMPLE-BASED SUPER-RESOLUTION FOR POINT-CLOUD VIDEO. IEEE SigPort. http://sigport.org/3459
Diogo C. Garcia, Tiago A. Fonseca, Ricardo L. de Queiroz, 2018. EXAMPLE-BASED SUPER-RESOLUTION FOR POINT-CLOUD VIDEO. Available at: http://sigport.org/3459.
Diogo C. Garcia, Tiago A. Fonseca, Ricardo L. de Queiroz. (2018). "EXAMPLE-BASED SUPER-RESOLUTION FOR POINT-CLOUD VIDEO." Web.
1. Diogo C. Garcia, Tiago A. Fonseca, Ricardo L. de Queiroz. EXAMPLE-BASED SUPER-RESOLUTION FOR POINT-CLOUD VIDEO [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3459

Affine invariants of vector fields

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Authors:
Jitka Kostkova, Jan Flusser, Tomas Suk
Submitted On:
4 October 2018 - 1:00pm
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Poster_ICIP18_color.pdf

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[1] Jitka Kostkova, Jan Flusser, Tomas Suk, "Affine invariants of vector fields", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3456. Accessed: Mar. 20, 2019.
@article{3456-18,
url = {http://sigport.org/3456},
author = {Jitka Kostkova; Jan Flusser; Tomas Suk },
publisher = {IEEE SigPort},
title = {Affine invariants of vector fields},
year = {2018} }
TY - EJOUR
T1 - Affine invariants of vector fields
AU - Jitka Kostkova; Jan Flusser; Tomas Suk
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3456
ER -
Jitka Kostkova, Jan Flusser, Tomas Suk. (2018). Affine invariants of vector fields. IEEE SigPort. http://sigport.org/3456
Jitka Kostkova, Jan Flusser, Tomas Suk, 2018. Affine invariants of vector fields. Available at: http://sigport.org/3456.
Jitka Kostkova, Jan Flusser, Tomas Suk. (2018). "Affine invariants of vector fields." Web.
1. Jitka Kostkova, Jan Flusser, Tomas Suk. Affine invariants of vector fields [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3456

Realistic Rendering of Material Aging for Artwork Objects


Material aging has a significant effect on the realistic rendering of artwork objects. Small deformations of the surface structure, color or texture variations contribute to the realistic look of artwork objects. These aging effects depend on material composition, object usage, weathering conditions, and a large number of other physical, biological, and chemical parameters.

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Authors:
A. Moutafidou, G. Adamopoulos, A. Drosou, D. Tzovaras, I. Fudos
Submitted On:
4 October 2018 - 1:00pm
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poster file (pdf)

presentation slides (pptx)

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[1] A. Moutafidou, G. Adamopoulos, A. Drosou, D. Tzovaras, I. Fudos, "Realistic Rendering of Material Aging for Artwork Objects", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3455. Accessed: Mar. 20, 2019.
@article{3455-18,
url = {http://sigport.org/3455},
author = {A. Moutafidou; G. Adamopoulos; A. Drosou; D. Tzovaras; I. Fudos },
publisher = {IEEE SigPort},
title = {Realistic Rendering of Material Aging for Artwork Objects},
year = {2018} }
TY - EJOUR
T1 - Realistic Rendering of Material Aging for Artwork Objects
AU - A. Moutafidou; G. Adamopoulos; A. Drosou; D. Tzovaras; I. Fudos
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3455
ER -
A. Moutafidou, G. Adamopoulos, A. Drosou, D. Tzovaras, I. Fudos. (2018). Realistic Rendering of Material Aging for Artwork Objects. IEEE SigPort. http://sigport.org/3455
A. Moutafidou, G. Adamopoulos, A. Drosou, D. Tzovaras, I. Fudos, 2018. Realistic Rendering of Material Aging for Artwork Objects. Available at: http://sigport.org/3455.
A. Moutafidou, G. Adamopoulos, A. Drosou, D. Tzovaras, I. Fudos. (2018). "Realistic Rendering of Material Aging for Artwork Objects." Web.
1. A. Moutafidou, G. Adamopoulos, A. Drosou, D. Tzovaras, I. Fudos. Realistic Rendering of Material Aging for Artwork Objects [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3455

Co-segmentation of Non-homogeneous Image Sets


In this paper, we formulate image co-segmentation as a classification problem in an unsupervised framework with the classes being the common foreground and the remaining regions in the image set. We first find a set of superpixels across all images with high feature similarity such that the constituent superpixels in individual images are spatially compact and label them as seed for the common foreground. Those superpixels with high background probability are labeled as respective seeds for multiple background classes.

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Authors:
Subhasis Chaudhuri, Rajbabu Velmurugan
Submitted On:
4 October 2018 - 12:57pm
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ICIP 2018 oral slides

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[1] Subhasis Chaudhuri, Rajbabu Velmurugan, "Co-segmentation of Non-homogeneous Image Sets", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3453. Accessed: Mar. 20, 2019.
@article{3453-18,
url = {http://sigport.org/3453},
author = {Subhasis Chaudhuri; Rajbabu Velmurugan },
publisher = {IEEE SigPort},
title = {Co-segmentation of Non-homogeneous Image Sets},
year = {2018} }
TY - EJOUR
T1 - Co-segmentation of Non-homogeneous Image Sets
AU - Subhasis Chaudhuri; Rajbabu Velmurugan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3453
ER -
Subhasis Chaudhuri, Rajbabu Velmurugan. (2018). Co-segmentation of Non-homogeneous Image Sets. IEEE SigPort. http://sigport.org/3453
Subhasis Chaudhuri, Rajbabu Velmurugan, 2018. Co-segmentation of Non-homogeneous Image Sets. Available at: http://sigport.org/3453.
Subhasis Chaudhuri, Rajbabu Velmurugan. (2018). "Co-segmentation of Non-homogeneous Image Sets." Web.
1. Subhasis Chaudhuri, Rajbabu Velmurugan. Co-segmentation of Non-homogeneous Image Sets [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3453

MOVING-CAMERA VIDEO SURVEILLANCE IN CLUTTERED ENVIRONMENTS USING DEEP FEATURES


This paper deals with the challenging problem of visual anomaly detection in a cluttered environment using videos acquired with a moving camera. The anomalies considered are abandoned objects.
A new method is proposed for comparing two videos (an anomalyfree reference video and a target one possibly with anomalies) by

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Authors:
Bruno M. Afonso, Lucas P. Cinelli, Lucas A. Thomaz, Allan F. da Silva, Eduardo A. B. da Silva, Sergio L. Netto
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4 October 2018 - 12:21pm
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poster-icip2018-final.pdf

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[1] Bruno M. Afonso, Lucas P. Cinelli, Lucas A. Thomaz, Allan F. da Silva, Eduardo A. B. da Silva, Sergio L. Netto, "MOVING-CAMERA VIDEO SURVEILLANCE IN CLUTTERED ENVIRONMENTS USING DEEP FEATURES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3451. Accessed: Mar. 20, 2019.
@article{3451-18,
url = {http://sigport.org/3451},
author = {Bruno M. Afonso; Lucas P. Cinelli; Lucas A. Thomaz; Allan F. da Silva; Eduardo A. B. da Silva; Sergio L. Netto },
publisher = {IEEE SigPort},
title = {MOVING-CAMERA VIDEO SURVEILLANCE IN CLUTTERED ENVIRONMENTS USING DEEP FEATURES},
year = {2018} }
TY - EJOUR
T1 - MOVING-CAMERA VIDEO SURVEILLANCE IN CLUTTERED ENVIRONMENTS USING DEEP FEATURES
AU - Bruno M. Afonso; Lucas P. Cinelli; Lucas A. Thomaz; Allan F. da Silva; Eduardo A. B. da Silva; Sergio L. Netto
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3451
ER -
Bruno M. Afonso, Lucas P. Cinelli, Lucas A. Thomaz, Allan F. da Silva, Eduardo A. B. da Silva, Sergio L. Netto. (2018). MOVING-CAMERA VIDEO SURVEILLANCE IN CLUTTERED ENVIRONMENTS USING DEEP FEATURES. IEEE SigPort. http://sigport.org/3451
Bruno M. Afonso, Lucas P. Cinelli, Lucas A. Thomaz, Allan F. da Silva, Eduardo A. B. da Silva, Sergio L. Netto, 2018. MOVING-CAMERA VIDEO SURVEILLANCE IN CLUTTERED ENVIRONMENTS USING DEEP FEATURES. Available at: http://sigport.org/3451.
Bruno M. Afonso, Lucas P. Cinelli, Lucas A. Thomaz, Allan F. da Silva, Eduardo A. B. da Silva, Sergio L. Netto. (2018). "MOVING-CAMERA VIDEO SURVEILLANCE IN CLUTTERED ENVIRONMENTS USING DEEP FEATURES." Web.
1. Bruno M. Afonso, Lucas P. Cinelli, Lucas A. Thomaz, Allan F. da Silva, Eduardo A. B. da Silva, Sergio L. Netto. MOVING-CAMERA VIDEO SURVEILLANCE IN CLUTTERED ENVIRONMENTS USING DEEP FEATURES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3451

Automatic ISP Image Quality Tuning Using Non-linear Optimization


Image Signal Processor (ISP) comprises of various blocks to reconstruct image sensor raw data to final image consumed by human visual system or computer vision applications. Each block typically has many tuning parameters due to the complexity of the operation. These need to be hand tuned by Image Quality (IQ) experts, which takes considerable amount of time. In this paper, we present an automatic IQ tuning using nonlinear optimization and automatic reference generation algorithms.

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Authors:
Jun Nishimura, Sushma Rao, Aleksandar Sutic, Chyuan-Tyng Wu, Gilad Michael
Submitted On:
4 October 2018 - 12:21pm
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ICIP2018_Poster_autoTune_TimoGerasimow.pdf

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[1] Jun Nishimura, Sushma Rao, Aleksandar Sutic, Chyuan-Tyng Wu, Gilad Michael, "Automatic ISP Image Quality Tuning Using Non-linear Optimization", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3450. Accessed: Mar. 20, 2019.
@article{3450-18,
url = {http://sigport.org/3450},
author = {Jun Nishimura; Sushma Rao; Aleksandar Sutic; Chyuan-Tyng Wu; Gilad Michael },
publisher = {IEEE SigPort},
title = {Automatic ISP Image Quality Tuning Using Non-linear Optimization},
year = {2018} }
TY - EJOUR
T1 - Automatic ISP Image Quality Tuning Using Non-linear Optimization
AU - Jun Nishimura; Sushma Rao; Aleksandar Sutic; Chyuan-Tyng Wu; Gilad Michael
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3450
ER -
Jun Nishimura, Sushma Rao, Aleksandar Sutic, Chyuan-Tyng Wu, Gilad Michael. (2018). Automatic ISP Image Quality Tuning Using Non-linear Optimization. IEEE SigPort. http://sigport.org/3450
Jun Nishimura, Sushma Rao, Aleksandar Sutic, Chyuan-Tyng Wu, Gilad Michael, 2018. Automatic ISP Image Quality Tuning Using Non-linear Optimization. Available at: http://sigport.org/3450.
Jun Nishimura, Sushma Rao, Aleksandar Sutic, Chyuan-Tyng Wu, Gilad Michael. (2018). "Automatic ISP Image Quality Tuning Using Non-linear Optimization." Web.
1. Jun Nishimura, Sushma Rao, Aleksandar Sutic, Chyuan-Tyng Wu, Gilad Michael. Automatic ISP Image Quality Tuning Using Non-linear Optimization [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3450

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