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Image/Video Processing

Joint image segmentation and classification with application to cluttered coral images

ying ma

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Authors:
ying ma
Bing Ouyang, Stephanie Farrington, Shujian Yu, Jone Reed, Jose Principe
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29 November 2018 - 9:51am
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[1] Bing Ouyang, Stephanie Farrington, Shujian Yu, Jone Reed, Jose Principe, "Joint image segmentation and classification with application to cluttered coral images", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3831. Accessed: Jul. 22, 2019.
@article{3831-18,
url = {http://sigport.org/3831},
author = {Bing Ouyang; Stephanie Farrington; Shujian Yu; Jone Reed; Jose Principe },
publisher = {IEEE SigPort},
title = {Joint image segmentation and classification with application to cluttered coral images},
year = {2018} }
TY - EJOUR
T1 - Joint image segmentation and classification with application to cluttered coral images
AU - Bing Ouyang; Stephanie Farrington; Shujian Yu; Jone Reed; Jose Principe
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3831
ER -
Bing Ouyang, Stephanie Farrington, Shujian Yu, Jone Reed, Jose Principe. (2018). Joint image segmentation and classification with application to cluttered coral images. IEEE SigPort. http://sigport.org/3831
Bing Ouyang, Stephanie Farrington, Shujian Yu, Jone Reed, Jose Principe, 2018. Joint image segmentation and classification with application to cluttered coral images. Available at: http://sigport.org/3831.
Bing Ouyang, Stephanie Farrington, Shujian Yu, Jone Reed, Jose Principe. (2018). "Joint image segmentation and classification with application to cluttered coral images." Web.
1. Bing Ouyang, Stephanie Farrington, Shujian Yu, Jone Reed, Jose Principe. Joint image segmentation and classification with application to cluttered coral images [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3831

Siamese Network with Multi-level Features for Patch-based Change Detection in Satellite Imagery

John Kerekes

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Authors:
John Kerekes
Faiz Ur Rahman, Bhavan Kumar Vasu, Jared Van Cor, John Kerekes, Andreas Savakis
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28 November 2018 - 2:51pm
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[1] Faiz Ur Rahman, Bhavan Kumar Vasu, Jared Van Cor, John Kerekes, Andreas Savakis, "Siamese Network with Multi-level Features for Patch-based Change Detection in Satellite Imagery", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3825. Accessed: Jul. 22, 2019.
@article{3825-18,
url = {http://sigport.org/3825},
author = {Faiz Ur Rahman; Bhavan Kumar Vasu; Jared Van Cor; John Kerekes; Andreas Savakis },
publisher = {IEEE SigPort},
title = {Siamese Network with Multi-level Features for Patch-based Change Detection in Satellite Imagery},
year = {2018} }
TY - EJOUR
T1 - Siamese Network with Multi-level Features for Patch-based Change Detection in Satellite Imagery
AU - Faiz Ur Rahman; Bhavan Kumar Vasu; Jared Van Cor; John Kerekes; Andreas Savakis
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3825
ER -
Faiz Ur Rahman, Bhavan Kumar Vasu, Jared Van Cor, John Kerekes, Andreas Savakis. (2018). Siamese Network with Multi-level Features for Patch-based Change Detection in Satellite Imagery. IEEE SigPort. http://sigport.org/3825
Faiz Ur Rahman, Bhavan Kumar Vasu, Jared Van Cor, John Kerekes, Andreas Savakis, 2018. Siamese Network with Multi-level Features for Patch-based Change Detection in Satellite Imagery. Available at: http://sigport.org/3825.
Faiz Ur Rahman, Bhavan Kumar Vasu, Jared Van Cor, John Kerekes, Andreas Savakis. (2018). "Siamese Network with Multi-level Features for Patch-based Change Detection in Satellite Imagery." Web.
1. Faiz Ur Rahman, Bhavan Kumar Vasu, Jared Van Cor, John Kerekes, Andreas Savakis. Siamese Network with Multi-level Features for Patch-based Change Detection in Satellite Imagery [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3825

Multi-View Frame Reconstruction with Conditional GAN

Tahmida Mahmud

Multi-view frame reconstruction is an important problem particularly when multiple frames are missing and past and future frames within the camera are far apart from the missing ones. Realistic coherent frames can still be reconstructed using corresponding frames from other overlapping cameras. We propose an adversarial approach to learn the
spatio-temporal representation of the missing frame using conditional Generative Adversarial Network (cGAN). The conditional input to each cGAN is the preceding or following

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Authors:
Tahmida Mahmud
Tahmida Mahmud, Mohammad Billah, Amit K. Roy-Chowdhury
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23 November 2018 - 3:53pm
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GlobalSIP.pdf

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[1] Tahmida Mahmud, Mohammad Billah, Amit K. Roy-Chowdhury, "Multi-View Frame Reconstruction with Conditional GAN", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3756. Accessed: Jul. 22, 2019.
@article{3756-18,
url = {http://sigport.org/3756},
author = {Tahmida Mahmud; Mohammad Billah; Amit K. Roy-Chowdhury },
publisher = {IEEE SigPort},
title = {Multi-View Frame Reconstruction with Conditional GAN},
year = {2018} }
TY - EJOUR
T1 - Multi-View Frame Reconstruction with Conditional GAN
AU - Tahmida Mahmud; Mohammad Billah; Amit K. Roy-Chowdhury
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3756
ER -
Tahmida Mahmud, Mohammad Billah, Amit K. Roy-Chowdhury. (2018). Multi-View Frame Reconstruction with Conditional GAN. IEEE SigPort. http://sigport.org/3756
Tahmida Mahmud, Mohammad Billah, Amit K. Roy-Chowdhury, 2018. Multi-View Frame Reconstruction with Conditional GAN. Available at: http://sigport.org/3756.
Tahmida Mahmud, Mohammad Billah, Amit K. Roy-Chowdhury. (2018). "Multi-View Frame Reconstruction with Conditional GAN." Web.
1. Tahmida Mahmud, Mohammad Billah, Amit K. Roy-Chowdhury. Multi-View Frame Reconstruction with Conditional GAN [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3756

COARSE-TO-FINE SEGMENTATION REFINEMENT AND MISSING SHAPE RECOVERY FOR HALIBUT FISH

Gaoang Wang

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Authors:
Gaoang Wang
Gaoang Wang, Jenq-Neng Hwang, Yiling Xu, Farron Wallace, Craig S. Rose
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20 November 2018 - 7:03pm
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poster_GlobalSIP2018.pdf

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[1] Gaoang Wang, Jenq-Neng Hwang, Yiling Xu, Farron Wallace, Craig S. Rose, "COARSE-TO-FINE SEGMENTATION REFINEMENT AND MISSING SHAPE RECOVERY FOR HALIBUT FISH", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3695. Accessed: Jul. 22, 2019.
@article{3695-18,
url = {http://sigport.org/3695},
author = {Gaoang Wang; Jenq-Neng Hwang; Yiling Xu; Farron Wallace; Craig S. Rose },
publisher = {IEEE SigPort},
title = {COARSE-TO-FINE SEGMENTATION REFINEMENT AND MISSING SHAPE RECOVERY FOR HALIBUT FISH},
year = {2018} }
TY - EJOUR
T1 - COARSE-TO-FINE SEGMENTATION REFINEMENT AND MISSING SHAPE RECOVERY FOR HALIBUT FISH
AU - Gaoang Wang; Jenq-Neng Hwang; Yiling Xu; Farron Wallace; Craig S. Rose
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3695
ER -
Gaoang Wang, Jenq-Neng Hwang, Yiling Xu, Farron Wallace, Craig S. Rose. (2018). COARSE-TO-FINE SEGMENTATION REFINEMENT AND MISSING SHAPE RECOVERY FOR HALIBUT FISH. IEEE SigPort. http://sigport.org/3695
Gaoang Wang, Jenq-Neng Hwang, Yiling Xu, Farron Wallace, Craig S. Rose, 2018. COARSE-TO-FINE SEGMENTATION REFINEMENT AND MISSING SHAPE RECOVERY FOR HALIBUT FISH. Available at: http://sigport.org/3695.
Gaoang Wang, Jenq-Neng Hwang, Yiling Xu, Farron Wallace, Craig S. Rose. (2018). "COARSE-TO-FINE SEGMENTATION REFINEMENT AND MISSING SHAPE RECOVERY FOR HALIBUT FISH." Web.
1. Gaoang Wang, Jenq-Neng Hwang, Yiling Xu, Farron Wallace, Craig S. Rose. COARSE-TO-FINE SEGMENTATION REFINEMENT AND MISSING SHAPE RECOVERY FOR HALIBUT FISH [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3695

FAST IMAGE MATCHING BASED ON FOURIER-MELLIN PHASE CORRELATION FOR TAG-LESS IDENTIFICATION OF MASS-PRODUCED PARTS

Rui Ishiyama

This paper proposes a fast technique for matching a query image to numerous database images under geometric variations in rotation, scale, and translation. Our proposed method extracts the Fourier-Mellin phase features from the images for invariant matching. The online matching process in our method is fast because it directly determines identification based on the correlation value between those features without the geometric alignment.

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Authors:
Rui Ishiyama
Toru Takahashi, Kengo Makino, Yuta Kudo
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20 November 2018 - 2:22am
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20181128-GlobalSIP2018_Poster_V3.pdf

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Movie-Demo-ShaftID_v4.zip

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[1] Toru Takahashi, Kengo Makino, Yuta Kudo , "FAST IMAGE MATCHING BASED ON FOURIER-MELLIN PHASE CORRELATION FOR TAG-LESS IDENTIFICATION OF MASS-PRODUCED PARTS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3693. Accessed: Jul. 22, 2019.
@article{3693-18,
url = {http://sigport.org/3693},
author = {Toru Takahashi; Kengo Makino; Yuta Kudo },
publisher = {IEEE SigPort},
title = {FAST IMAGE MATCHING BASED ON FOURIER-MELLIN PHASE CORRELATION FOR TAG-LESS IDENTIFICATION OF MASS-PRODUCED PARTS},
year = {2018} }
TY - EJOUR
T1 - FAST IMAGE MATCHING BASED ON FOURIER-MELLIN PHASE CORRELATION FOR TAG-LESS IDENTIFICATION OF MASS-PRODUCED PARTS
AU - Toru Takahashi; Kengo Makino; Yuta Kudo
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3693
ER -
Toru Takahashi, Kengo Makino, Yuta Kudo . (2018). FAST IMAGE MATCHING BASED ON FOURIER-MELLIN PHASE CORRELATION FOR TAG-LESS IDENTIFICATION OF MASS-PRODUCED PARTS. IEEE SigPort. http://sigport.org/3693
Toru Takahashi, Kengo Makino, Yuta Kudo , 2018. FAST IMAGE MATCHING BASED ON FOURIER-MELLIN PHASE CORRELATION FOR TAG-LESS IDENTIFICATION OF MASS-PRODUCED PARTS. Available at: http://sigport.org/3693.
Toru Takahashi, Kengo Makino, Yuta Kudo . (2018). "FAST IMAGE MATCHING BASED ON FOURIER-MELLIN PHASE CORRELATION FOR TAG-LESS IDENTIFICATION OF MASS-PRODUCED PARTS." Web.
1. Toru Takahashi, Kengo Makino, Yuta Kudo . FAST IMAGE MATCHING BASED ON FOURIER-MELLIN PHASE CORRELATION FOR TAG-LESS IDENTIFICATION OF MASS-PRODUCED PARTS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3693

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
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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: Jul. 22, 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

Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence

Dhruv Shah
Ajit Rajwade

Most existing work in designing sensing matrices for compressive recovery is based on optimizing some quality factor, such as mutual coherence, average coherence or the restricted isometry constant (RIC), of the sensing matrix. In this paper, we report anomalous results that show that such a design is not always guaranteed to improve reconstruction results.

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Authors:
Dhruv Shah
Ajit Rajwade
Dhruv Shah, Alankar Kotwal, Ajit Rajwade
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20 November 2018 - 2:42am
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globalsip2018_poster_v3.pdf

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[1] Dhruv Shah, Alankar Kotwal, Ajit Rajwade, "Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3670. Accessed: Jul. 22, 2019.
@article{3670-18,
url = {http://sigport.org/3670},
author = {Dhruv Shah; Alankar Kotwal; Ajit Rajwade },
publisher = {IEEE SigPort},
title = {Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence},
year = {2018} }
TY - EJOUR
T1 - Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence
AU - Dhruv Shah; Alankar Kotwal; Ajit Rajwade
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3670
ER -
Dhruv Shah, Alankar Kotwal, Ajit Rajwade. (2018). Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence. IEEE SigPort. http://sigport.org/3670
Dhruv Shah, Alankar Kotwal, Ajit Rajwade, 2018. Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence. Available at: http://sigport.org/3670.
Dhruv Shah, Alankar Kotwal, Ajit Rajwade. (2018). "Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence." Web.
1. Dhruv Shah, Alankar Kotwal, Ajit Rajwade. Designing Constrained Projections for Compressed Sensing: Mean Errors and Anomalies with Coherence [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3670

Distortion-Robust Spherical Camera Motion Estimation via Dense Optical Flow

Sarthak Pathak

Conventional techniques for frame-to-frame camera motion estimation rely on tracking a set of sparse feature points. However, images taken from spherical cameras have high distortion which can induce mistakes in feature point tracking, offsetting the advantage of their large fields-of-view. Hence, in this research, we attempt a novel approach of using dense optical flow for distortion-robust spherical camera motion estimation. Dense optical flow incorporates smoothing terms and is free of local outliers. It encodes the camera motion as well as dense 3D information.

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Authors:
Sarthak Pathak
Alessandro Moro, Hiromitsu Fujii, Atsushi Yamashita, Hajime Asama
Submitted On:
18 October 2018 - 2:08am
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Conference Poster

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[1] Alessandro Moro, Hiromitsu Fujii, Atsushi Yamashita, Hajime Asama, "Distortion-Robust Spherical Camera Motion Estimation via Dense Optical Flow", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3667. Accessed: Jul. 22, 2019.
@article{3667-18,
url = {http://sigport.org/3667},
author = {Alessandro Moro; Hiromitsu Fujii; Atsushi Yamashita; Hajime Asama },
publisher = {IEEE SigPort},
title = {Distortion-Robust Spherical Camera Motion Estimation via Dense Optical Flow},
year = {2018} }
TY - EJOUR
T1 - Distortion-Robust Spherical Camera Motion Estimation via Dense Optical Flow
AU - Alessandro Moro; Hiromitsu Fujii; Atsushi Yamashita; Hajime Asama
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3667
ER -
Alessandro Moro, Hiromitsu Fujii, Atsushi Yamashita, Hajime Asama. (2018). Distortion-Robust Spherical Camera Motion Estimation via Dense Optical Flow. IEEE SigPort. http://sigport.org/3667
Alessandro Moro, Hiromitsu Fujii, Atsushi Yamashita, Hajime Asama, 2018. Distortion-Robust Spherical Camera Motion Estimation via Dense Optical Flow. Available at: http://sigport.org/3667.
Alessandro Moro, Hiromitsu Fujii, Atsushi Yamashita, Hajime Asama. (2018). "Distortion-Robust Spherical Camera Motion Estimation via Dense Optical Flow." Web.
1. Alessandro Moro, Hiromitsu Fujii, Atsushi Yamashita, Hajime Asama. Distortion-Robust Spherical Camera Motion Estimation via Dense Optical Flow [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3667

Recurrent and Dynamic Models for Predicting Streaming Video Quality of Experience

Christos Bampis

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Authors:
Christos Bampis
Christos Bampis, Zhi Li, Ioannis Katsavounidis, Alan C. Bovik
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14 October 2018 - 10:55am
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GNARX_ICIP_2018_Poster.pdf

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[1] Christos Bampis, Zhi Li, Ioannis Katsavounidis, Alan C. Bovik, "Recurrent and Dynamic Models for Predicting Streaming Video Quality of Experience", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3664. Accessed: Jul. 22, 2019.
@article{3664-18,
url = {http://sigport.org/3664},
author = {Christos Bampis; Zhi Li; Ioannis Katsavounidis; Alan C. Bovik },
publisher = {IEEE SigPort},
title = {Recurrent and Dynamic Models for Predicting Streaming Video Quality of Experience},
year = {2018} }
TY - EJOUR
T1 - Recurrent and Dynamic Models for Predicting Streaming Video Quality of Experience
AU - Christos Bampis; Zhi Li; Ioannis Katsavounidis; Alan C. Bovik
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3664
ER -
Christos Bampis, Zhi Li, Ioannis Katsavounidis, Alan C. Bovik. (2018). Recurrent and Dynamic Models for Predicting Streaming Video Quality of Experience. IEEE SigPort. http://sigport.org/3664
Christos Bampis, Zhi Li, Ioannis Katsavounidis, Alan C. Bovik, 2018. Recurrent and Dynamic Models for Predicting Streaming Video Quality of Experience. Available at: http://sigport.org/3664.
Christos Bampis, Zhi Li, Ioannis Katsavounidis, Alan C. Bovik. (2018). "Recurrent and Dynamic Models for Predicting Streaming Video Quality of Experience." Web.
1. Christos Bampis, Zhi Li, Ioannis Katsavounidis, Alan C. Bovik. Recurrent and Dynamic Models for Predicting Streaming Video Quality of Experience [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3664

Profile Hidden Markov Models for Foreground Object Modelling

Ioannis Kazantzidis

Accurate background/foreground segmentation is a preliminary process essential to most visual surveillance applications. With the increasing use of freely moving cameras, strategies have been proposed to refine initial segmentation. In this paper, it is proposed to exploit the Vide-omics paradigm, and Profile Hidden Markov Models in particular, to create a new type of object descriptors relying on spatiotemporal information. Performance of the proposed methodology has been evaluated using a standard dataset of videos captured by moving cameras.

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Authors:
Ioannis Kazantzidis
Francisco Florez-Revuelta, Jean-Christophe Nebel
Submitted On:
12 October 2018 - 7:02am
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Bioinformatics-inspired video analysis

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[1] Francisco Florez-Revuelta, Jean-Christophe Nebel, "Profile Hidden Markov Models for Foreground Object Modelling", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3663. Accessed: Jul. 22, 2019.
@article{3663-18,
url = {http://sigport.org/3663},
author = {Francisco Florez-Revuelta; Jean-Christophe Nebel },
publisher = {IEEE SigPort},
title = {Profile Hidden Markov Models for Foreground Object Modelling},
year = {2018} }
TY - EJOUR
T1 - Profile Hidden Markov Models for Foreground Object Modelling
AU - Francisco Florez-Revuelta; Jean-Christophe Nebel
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3663
ER -
Francisco Florez-Revuelta, Jean-Christophe Nebel. (2018). Profile Hidden Markov Models for Foreground Object Modelling. IEEE SigPort. http://sigport.org/3663
Francisco Florez-Revuelta, Jean-Christophe Nebel, 2018. Profile Hidden Markov Models for Foreground Object Modelling. Available at: http://sigport.org/3663.
Francisco Florez-Revuelta, Jean-Christophe Nebel. (2018). "Profile Hidden Markov Models for Foreground Object Modelling." Web.
1. Francisco Florez-Revuelta, Jean-Christophe Nebel. Profile Hidden Markov Models for Foreground Object Modelling [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3663

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