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ICIP 2020

ICIP 2020 is a fully virtual conference. The International Conference on Image Processing (ICIP), sponsored by the IEEE Signal Processing Society, is the premier forum for the presentation of technological advances and research results in the fields of theoretical, experimental, and applied image and video processing. ICIP has been held annually since 1994, brings together leading engineers and scientists in image and video processing from around the world. Visit website

Joint Content-Adaptive Dictionary Learning and Sparse Selective Extrapolation for Cross-Spectral Image Reconstruction

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3 November 2020 - 6:49am
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[1] , "Joint Content-Adaptive Dictionary Learning and Sparse Selective Extrapolation for Cross-Spectral Image Reconstruction", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5517. Accessed: Nov. 26, 2020.
@article{5517-20,
url = {http://sigport.org/5517},
author = { },
publisher = {IEEE SigPort},
title = {Joint Content-Adaptive Dictionary Learning and Sparse Selective Extrapolation for Cross-Spectral Image Reconstruction},
year = {2020} }
TY - EJOUR
T1 - Joint Content-Adaptive Dictionary Learning and Sparse Selective Extrapolation for Cross-Spectral Image Reconstruction
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5517
ER -
. (2020). Joint Content-Adaptive Dictionary Learning and Sparse Selective Extrapolation for Cross-Spectral Image Reconstruction. IEEE SigPort. http://sigport.org/5517
, 2020. Joint Content-Adaptive Dictionary Learning and Sparse Selective Extrapolation for Cross-Spectral Image Reconstruction. Available at: http://sigport.org/5517.
. (2020). "Joint Content-Adaptive Dictionary Learning and Sparse Selective Extrapolation for Cross-Spectral Image Reconstruction." Web.
1. . Joint Content-Adaptive Dictionary Learning and Sparse Selective Extrapolation for Cross-Spectral Image Reconstruction [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5517

Deep Learning Based Cross-Spectral Disparity Estimation for Stereo Imaging

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3 November 2020 - 6:46am
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[1] , "Deep Learning Based Cross-Spectral Disparity Estimation for Stereo Imaging", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5516. Accessed: Nov. 26, 2020.
@article{5516-20,
url = {http://sigport.org/5516},
author = { },
publisher = {IEEE SigPort},
title = {Deep Learning Based Cross-Spectral Disparity Estimation for Stereo Imaging},
year = {2020} }
TY - EJOUR
T1 - Deep Learning Based Cross-Spectral Disparity Estimation for Stereo Imaging
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5516
ER -
. (2020). Deep Learning Based Cross-Spectral Disparity Estimation for Stereo Imaging. IEEE SigPort. http://sigport.org/5516
, 2020. Deep Learning Based Cross-Spectral Disparity Estimation for Stereo Imaging. Available at: http://sigport.org/5516.
. (2020). "Deep Learning Based Cross-Spectral Disparity Estimation for Stereo Imaging." Web.
1. . Deep Learning Based Cross-Spectral Disparity Estimation for Stereo Imaging [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5516

A Spatio-Angular Binary Descriptor for Fast Light Field Inter View Matching


Light fields are able to capture light rays from a scene arriving at different angles, effectively creating multiple perspective views of the same scene. Thus, one of the flagship applications of light fields is to estimate the captured scene geometry, which can notably be achieved by establishing correspondences between the perspective views, usually in the form of a disparity map. Such correspondence estimation has been a long standing research topic in computer vision, with application to stereo vision or optical flow.

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Martin Alain, Aljosa Smolic
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3 November 2020 - 6:44am
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[1] Martin Alain, Aljosa Smolic, "A Spatio-Angular Binary Descriptor for Fast Light Field Inter View Matching", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5515. Accessed: Nov. 26, 2020.
@article{5515-20,
url = {http://sigport.org/5515},
author = {Martin Alain; Aljosa Smolic },
publisher = {IEEE SigPort},
title = {A Spatio-Angular Binary Descriptor for Fast Light Field Inter View Matching},
year = {2020} }
TY - EJOUR
T1 - A Spatio-Angular Binary Descriptor for Fast Light Field Inter View Matching
AU - Martin Alain; Aljosa Smolic
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5515
ER -
Martin Alain, Aljosa Smolic. (2020). A Spatio-Angular Binary Descriptor for Fast Light Field Inter View Matching. IEEE SigPort. http://sigport.org/5515
Martin Alain, Aljosa Smolic, 2020. A Spatio-Angular Binary Descriptor for Fast Light Field Inter View Matching. Available at: http://sigport.org/5515.
Martin Alain, Aljosa Smolic. (2020). "A Spatio-Angular Binary Descriptor for Fast Light Field Inter View Matching." Web.
1. Martin Alain, Aljosa Smolic. A Spatio-Angular Binary Descriptor for Fast Light Field Inter View Matching [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5515

MaskPan: mask prior guide pansharpening network

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3 November 2020 - 6:06am
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[1] , "MaskPan: mask prior guide pansharpening network", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5514. Accessed: Nov. 26, 2020.
@article{5514-20,
url = {http://sigport.org/5514},
author = { },
publisher = {IEEE SigPort},
title = {MaskPan: mask prior guide pansharpening network},
year = {2020} }
TY - EJOUR
T1 - MaskPan: mask prior guide pansharpening network
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5514
ER -
. (2020). MaskPan: mask prior guide pansharpening network. IEEE SigPort. http://sigport.org/5514
, 2020. MaskPan: mask prior guide pansharpening network. Available at: http://sigport.org/5514.
. (2020). "MaskPan: mask prior guide pansharpening network." Web.
1. . MaskPan: mask prior guide pansharpening network [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5514

B-SPLINE LEVEL SET FOR DROSOPHILA IMAGE SEGMENTATION


Segmentation of biological images is a challenging task, due to non convex shapes, intensity inhomogeneity and clustered cells. To address these issues, a new algorithm is proposed based on the B-spline level set method. The implicit function of the level set is modelled as a continuous parametric function represented with the B-spline basis. It is different from the discrete formulation associated with conventional level set. In this paper the proposed framework takes into account properties of biological images.

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Authors:
Rim Rahali, Yassine Ben Salem, Noura Dridi, Hassen Dahman
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3 November 2020 - 5:46am
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[1] Rim Rahali, Yassine Ben Salem, Noura Dridi, Hassen Dahman, "B-SPLINE LEVEL SET FOR DROSOPHILA IMAGE SEGMENTATION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5513. Accessed: Nov. 26, 2020.
@article{5513-20,
url = {http://sigport.org/5513},
author = {Rim Rahali; Yassine Ben Salem; Noura Dridi; Hassen Dahman },
publisher = {IEEE SigPort},
title = {B-SPLINE LEVEL SET FOR DROSOPHILA IMAGE SEGMENTATION},
year = {2020} }
TY - EJOUR
T1 - B-SPLINE LEVEL SET FOR DROSOPHILA IMAGE SEGMENTATION
AU - Rim Rahali; Yassine Ben Salem; Noura Dridi; Hassen Dahman
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5513
ER -
Rim Rahali, Yassine Ben Salem, Noura Dridi, Hassen Dahman. (2020). B-SPLINE LEVEL SET FOR DROSOPHILA IMAGE SEGMENTATION. IEEE SigPort. http://sigport.org/5513
Rim Rahali, Yassine Ben Salem, Noura Dridi, Hassen Dahman, 2020. B-SPLINE LEVEL SET FOR DROSOPHILA IMAGE SEGMENTATION. Available at: http://sigport.org/5513.
Rim Rahali, Yassine Ben Salem, Noura Dridi, Hassen Dahman. (2020). "B-SPLINE LEVEL SET FOR DROSOPHILA IMAGE SEGMENTATION." Web.
1. Rim Rahali, Yassine Ben Salem, Noura Dridi, Hassen Dahman. B-SPLINE LEVEL SET FOR DROSOPHILA IMAGE SEGMENTATION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5513

MULTI-SCALE EXPLAINABLE FEATURE LEARNING FOR PATHOLOGICAL IMAGE ANALYSIS USING CONVOLUTIONAL NEURAL NETWORKS

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Authors:
Masahiro Murakawa, Hirokazu Nosato, Hidenori Sakanashi
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3 November 2020 - 5:33am
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[1] Masahiro Murakawa, Hirokazu Nosato, Hidenori Sakanashi, "MULTI-SCALE EXPLAINABLE FEATURE LEARNING FOR PATHOLOGICAL IMAGE ANALYSIS USING CONVOLUTIONAL NEURAL NETWORKS", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5512. Accessed: Nov. 26, 2020.
@article{5512-20,
url = {http://sigport.org/5512},
author = {Masahiro Murakawa; Hirokazu Nosato; Hidenori Sakanashi },
publisher = {IEEE SigPort},
title = {MULTI-SCALE EXPLAINABLE FEATURE LEARNING FOR PATHOLOGICAL IMAGE ANALYSIS USING CONVOLUTIONAL NEURAL NETWORKS},
year = {2020} }
TY - EJOUR
T1 - MULTI-SCALE EXPLAINABLE FEATURE LEARNING FOR PATHOLOGICAL IMAGE ANALYSIS USING CONVOLUTIONAL NEURAL NETWORKS
AU - Masahiro Murakawa; Hirokazu Nosato; Hidenori Sakanashi
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5512
ER -
Masahiro Murakawa, Hirokazu Nosato, Hidenori Sakanashi. (2020). MULTI-SCALE EXPLAINABLE FEATURE LEARNING FOR PATHOLOGICAL IMAGE ANALYSIS USING CONVOLUTIONAL NEURAL NETWORKS. IEEE SigPort. http://sigport.org/5512
Masahiro Murakawa, Hirokazu Nosato, Hidenori Sakanashi, 2020. MULTI-SCALE EXPLAINABLE FEATURE LEARNING FOR PATHOLOGICAL IMAGE ANALYSIS USING CONVOLUTIONAL NEURAL NETWORKS. Available at: http://sigport.org/5512.
Masahiro Murakawa, Hirokazu Nosato, Hidenori Sakanashi. (2020). "MULTI-SCALE EXPLAINABLE FEATURE LEARNING FOR PATHOLOGICAL IMAGE ANALYSIS USING CONVOLUTIONAL NEURAL NETWORKS." Web.
1. Masahiro Murakawa, Hirokazu Nosato, Hidenori Sakanashi. MULTI-SCALE EXPLAINABLE FEATURE LEARNING FOR PATHOLOGICAL IMAGE ANALYSIS USING CONVOLUTIONAL NEURAL NETWORKS [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5512

Improving PSNR-Based Quality Metrics Performance for Point Cloud Geometry


An increased interest in immersive applications has drawn attention to emerging 3D imaging representation formats, notably light fields and point clouds (PCs). Nowadays, PCs are one of the most popular 3D media formats, due to recent developments in PC acquisition, namely depth sensors and signal processing algorithms. To obtain high fidelity 3D representations of visual scenes a huge amount of PC data is typically acquired, which demands efficient compression solutions.

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Alireza Javaheri, Catarina Brites, Fernando Pereira, Joao Ascenso
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3 November 2020 - 5:05am
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[1] Alireza Javaheri, Catarina Brites, Fernando Pereira, Joao Ascenso, "Improving PSNR-Based Quality Metrics Performance for Point Cloud Geometry", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5511. Accessed: Nov. 26, 2020.
@article{5511-20,
url = {http://sigport.org/5511},
author = {Alireza Javaheri; Catarina Brites; Fernando Pereira; Joao Ascenso },
publisher = {IEEE SigPort},
title = {Improving PSNR-Based Quality Metrics Performance for Point Cloud Geometry},
year = {2020} }
TY - EJOUR
T1 - Improving PSNR-Based Quality Metrics Performance for Point Cloud Geometry
AU - Alireza Javaheri; Catarina Brites; Fernando Pereira; Joao Ascenso
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5511
ER -
Alireza Javaheri, Catarina Brites, Fernando Pereira, Joao Ascenso. (2020). Improving PSNR-Based Quality Metrics Performance for Point Cloud Geometry. IEEE SigPort. http://sigport.org/5511
Alireza Javaheri, Catarina Brites, Fernando Pereira, Joao Ascenso, 2020. Improving PSNR-Based Quality Metrics Performance for Point Cloud Geometry. Available at: http://sigport.org/5511.
Alireza Javaheri, Catarina Brites, Fernando Pereira, Joao Ascenso. (2020). "Improving PSNR-Based Quality Metrics Performance for Point Cloud Geometry." Web.
1. Alireza Javaheri, Catarina Brites, Fernando Pereira, Joao Ascenso. Improving PSNR-Based Quality Metrics Performance for Point Cloud Geometry [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5511

Folding-Based Compression of Point Cloud Attributes


Existing techniques to compress point cloud attributes leverage either geometric or video-based compression tools. We explore a radically different approach inspired by recent advances in point cloud representation learning. Point clouds can be interpreted as 2D manifolds in 3D space. Specifically, we fold a 2D grid onto a point cloud and we map attributes from the point cloud onto the folded 2D grid using a novel optimized mapping method. This mapping results in an image, which opens a way to apply existing image processing techniques on point cloud attributes.

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Authors:
Giuseppe Valenzise, Frederic Dufaux
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3 November 2020 - 4:58am
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[1] Giuseppe Valenzise, Frederic Dufaux, "Folding-Based Compression of Point Cloud Attributes", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5510. Accessed: Nov. 26, 2020.
@article{5510-20,
url = {http://sigport.org/5510},
author = {Giuseppe Valenzise; Frederic Dufaux },
publisher = {IEEE SigPort},
title = {Folding-Based Compression of Point Cloud Attributes},
year = {2020} }
TY - EJOUR
T1 - Folding-Based Compression of Point Cloud Attributes
AU - Giuseppe Valenzise; Frederic Dufaux
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5510
ER -
Giuseppe Valenzise, Frederic Dufaux. (2020). Folding-Based Compression of Point Cloud Attributes. IEEE SigPort. http://sigport.org/5510
Giuseppe Valenzise, Frederic Dufaux, 2020. Folding-Based Compression of Point Cloud Attributes. Available at: http://sigport.org/5510.
Giuseppe Valenzise, Frederic Dufaux. (2020). "Folding-Based Compression of Point Cloud Attributes." Web.
1. Giuseppe Valenzise, Frederic Dufaux. Folding-Based Compression of Point Cloud Attributes [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5510

Optimal Measurement Budget Allocation for Particle Filtering


Particle filtering is a powerful tool for target tracking. When the budget for observations is restricted, it is necessary to reduce the measurements to a limited amount of samples carefully selected. A discrete stochastic nonlinear dynamical system is studied over a finite time horizon. The problem of selecting the optimal measurement times for particle filtering is formalized as a combinatorial optimization problem. We propose an approximated solution based on the nesting of a genetic algorithm, a Monte Carlo algorithm and a particle filter.

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Authors:
Antoine Aspeel, Amaury Gouverneur, Raphaël M. Jungers, Benoit Macq
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3 November 2020 - 4:35am
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[1] Antoine Aspeel, Amaury Gouverneur, Raphaël M. Jungers, Benoit Macq, "Optimal Measurement Budget Allocation for Particle Filtering", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5509. Accessed: Nov. 26, 2020.
@article{5509-20,
url = {http://sigport.org/5509},
author = {Antoine Aspeel; Amaury Gouverneur; Raphaël M. Jungers; Benoit Macq },
publisher = {IEEE SigPort},
title = {Optimal Measurement Budget Allocation for Particle Filtering},
year = {2020} }
TY - EJOUR
T1 - Optimal Measurement Budget Allocation for Particle Filtering
AU - Antoine Aspeel; Amaury Gouverneur; Raphaël M. Jungers; Benoit Macq
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5509
ER -
Antoine Aspeel, Amaury Gouverneur, Raphaël M. Jungers, Benoit Macq. (2020). Optimal Measurement Budget Allocation for Particle Filtering. IEEE SigPort. http://sigport.org/5509
Antoine Aspeel, Amaury Gouverneur, Raphaël M. Jungers, Benoit Macq, 2020. Optimal Measurement Budget Allocation for Particle Filtering. Available at: http://sigport.org/5509.
Antoine Aspeel, Amaury Gouverneur, Raphaël M. Jungers, Benoit Macq. (2020). "Optimal Measurement Budget Allocation for Particle Filtering." Web.
1. Antoine Aspeel, Amaury Gouverneur, Raphaël M. Jungers, Benoit Macq. Optimal Measurement Budget Allocation for Particle Filtering [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5509

Super-resolution of 3D MRI corrupted by heavy noise with the Median Filter Transform


The acquisition of 3D MRIs is adversely affected by many degrading factors including low spatial resolution and noise. Image enhancement techniques are commonplace, but there are few proposals that address the increase of the spatial resolution and noise removal at the same time. An algorithm to address this vital need is proposed in this presented work. The proposal tiles the 3D image space into parallelepipeds, so that a median filter is applied in each parallelepiped. The results obtained from several such tilings are then combined by a subsequent median computation.

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Authors:
Karl Thurnhofer-Hemsi, Ezequiel López-Rubio, Núria Roé-Vellvé, Lipika Deka
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3 November 2020 - 4:07am
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[1] Karl Thurnhofer-Hemsi, Ezequiel López-Rubio, Núria Roé-Vellvé, Lipika Deka, "Super-resolution of 3D MRI corrupted by heavy noise with the Median Filter Transform", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5508. Accessed: Nov. 26, 2020.
@article{5508-20,
url = {http://sigport.org/5508},
author = {Karl Thurnhofer-Hemsi; Ezequiel López-Rubio; Núria Roé-Vellvé; Lipika Deka },
publisher = {IEEE SigPort},
title = {Super-resolution of 3D MRI corrupted by heavy noise with the Median Filter Transform},
year = {2020} }
TY - EJOUR
T1 - Super-resolution of 3D MRI corrupted by heavy noise with the Median Filter Transform
AU - Karl Thurnhofer-Hemsi; Ezequiel López-Rubio; Núria Roé-Vellvé; Lipika Deka
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5508
ER -
Karl Thurnhofer-Hemsi, Ezequiel López-Rubio, Núria Roé-Vellvé, Lipika Deka. (2020). Super-resolution of 3D MRI corrupted by heavy noise with the Median Filter Transform. IEEE SigPort. http://sigport.org/5508
Karl Thurnhofer-Hemsi, Ezequiel López-Rubio, Núria Roé-Vellvé, Lipika Deka, 2020. Super-resolution of 3D MRI corrupted by heavy noise with the Median Filter Transform. Available at: http://sigport.org/5508.
Karl Thurnhofer-Hemsi, Ezequiel López-Rubio, Núria Roé-Vellvé, Lipika Deka. (2020). "Super-resolution of 3D MRI corrupted by heavy noise with the Median Filter Transform." Web.
1. Karl Thurnhofer-Hemsi, Ezequiel López-Rubio, Núria Roé-Vellvé, Lipika Deka. Super-resolution of 3D MRI corrupted by heavy noise with the Median Filter Transform [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5508

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