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ICIP 2017

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.

Immersive Optical-See-Through Augmented Reality (Keynote Talk)


Immersive Optical-See-Through Augmented Reality

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Authors:
Kari Pulli
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11 October 2017 - 2:41pm
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ICIP_2017_Meta_AR_small.pdf

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[1] Kari Pulli, "Immersive Optical-See-Through Augmented Reality (Keynote Talk)", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2261. Accessed: Oct. 18, 2017.
@article{2261-17,
url = {http://sigport.org/2261},
author = {Kari Pulli },
publisher = {IEEE SigPort},
title = {Immersive Optical-See-Through Augmented Reality (Keynote Talk)},
year = {2017} }
TY - EJOUR
T1 - Immersive Optical-See-Through Augmented Reality (Keynote Talk)
AU - Kari Pulli
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2261
ER -
Kari Pulli. (2017). Immersive Optical-See-Through Augmented Reality (Keynote Talk). IEEE SigPort. http://sigport.org/2261
Kari Pulli, 2017. Immersive Optical-See-Through Augmented Reality (Keynote Talk). Available at: http://sigport.org/2261.
Kari Pulli. (2017). "Immersive Optical-See-Through Augmented Reality (Keynote Talk)." Web.
1. Kari Pulli. Immersive Optical-See-Through Augmented Reality (Keynote Talk) [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2261

Sparse Modeling


Sparse Modeling in Image Processing and Deep Learning

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Authors:
Michael Elad
Submitted On:
11 October 2017 - 3:05pm
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ICIP_KeyNote_Talk_small size.pdf

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[1] Michael Elad, "Sparse Modeling ", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2260. Accessed: Oct. 18, 2017.
@article{2260-17,
url = {http://sigport.org/2260},
author = {Michael Elad },
publisher = {IEEE SigPort},
title = {Sparse Modeling },
year = {2017} }
TY - EJOUR
T1 - Sparse Modeling
AU - Michael Elad
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2260
ER -
Michael Elad. (2017). Sparse Modeling . IEEE SigPort. http://sigport.org/2260
Michael Elad, 2017. Sparse Modeling . Available at: http://sigport.org/2260.
Michael Elad. (2017). "Sparse Modeling ." Web.
1. Michael Elad. Sparse Modeling [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2260

ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding


The alternating direction method of multipliers (ADMM) has been widely used for a very wide variety of imaging inverse problems. One of the disadvantages of this method, however, is the need to select an algorithm parameter, the penalty parameter, that has a significant effect on the rate of convergence of the algorithm. Although a number of heuristic methods have been proposed, as yet there is no general theory providing a good choice of this parameter for all problems.

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Authors:
Youzuo Lin, Brendt Wohlberg, Velimir Vesselinov
Submitted On:
3 October 2017 - 6:45pm
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[1] Youzuo Lin, Brendt Wohlberg, Velimir Vesselinov, "ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2254. Accessed: Oct. 18, 2017.
@article{2254-17,
url = {http://sigport.org/2254},
author = {Youzuo Lin; Brendt Wohlberg; Velimir Vesselinov },
publisher = {IEEE SigPort},
title = {ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding},
year = {2017} }
TY - EJOUR
T1 - ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding
AU - Youzuo Lin; Brendt Wohlberg; Velimir Vesselinov
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2254
ER -
Youzuo Lin, Brendt Wohlberg, Velimir Vesselinov. (2017). ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding. IEEE SigPort. http://sigport.org/2254
Youzuo Lin, Brendt Wohlberg, Velimir Vesselinov, 2017. ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding. Available at: http://sigport.org/2254.
Youzuo Lin, Brendt Wohlberg, Velimir Vesselinov. (2017). "ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding." Web.
1. Youzuo Lin, Brendt Wohlberg, Velimir Vesselinov. ADMM Penalty Parameter Selection with Krylov Subspace Recycling Technique for Sparse Coding [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2254

GLAND SEGMENTATION GUIDED BY GLANDULAR STRUCTURES: A LEVEL SET FRAMEWORK WITH TWO LEVELS

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Authors:
Ji Bao, Hong Bu
Submitted On:
3 October 2017 - 4:28am
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ICIP_poster3433.pdf

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[1] Ji Bao, Hong Bu, "GLAND SEGMENTATION GUIDED BY GLANDULAR STRUCTURES: A LEVEL SET FRAMEWORK WITH TWO LEVELS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2253. Accessed: Oct. 18, 2017.
@article{2253-17,
url = {http://sigport.org/2253},
author = {Ji Bao; Hong Bu },
publisher = {IEEE SigPort},
title = {GLAND SEGMENTATION GUIDED BY GLANDULAR STRUCTURES: A LEVEL SET FRAMEWORK WITH TWO LEVELS},
year = {2017} }
TY - EJOUR
T1 - GLAND SEGMENTATION GUIDED BY GLANDULAR STRUCTURES: A LEVEL SET FRAMEWORK WITH TWO LEVELS
AU - Ji Bao; Hong Bu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2253
ER -
Ji Bao, Hong Bu. (2017). GLAND SEGMENTATION GUIDED BY GLANDULAR STRUCTURES: A LEVEL SET FRAMEWORK WITH TWO LEVELS. IEEE SigPort. http://sigport.org/2253
Ji Bao, Hong Bu, 2017. GLAND SEGMENTATION GUIDED BY GLANDULAR STRUCTURES: A LEVEL SET FRAMEWORK WITH TWO LEVELS. Available at: http://sigport.org/2253.
Ji Bao, Hong Bu. (2017). "GLAND SEGMENTATION GUIDED BY GLANDULAR STRUCTURES: A LEVEL SET FRAMEWORK WITH TWO LEVELS." Web.
1. Ji Bao, Hong Bu. GLAND SEGMENTATION GUIDED BY GLANDULAR STRUCTURES: A LEVEL SET FRAMEWORK WITH TWO LEVELS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2253

Probabilistic Approach to People-Centric Photo Selection and Sequencing


We present a crowdsourcing (CS) study to examine how specific attributes probabilistically affect the selection and sequencing of images from personal photo collections. 13 image attributes are explored, including 7 people-centric properties. We first propose a novel dataset shaping technique based on Mixed Integer Linear Programming (MILP) to identify a subset of photos in which the attributes of interest are uniformly distributed and minimally correlated.

poster.pdf

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Paper Details

Authors:
Vassilios Vonikakis, Ramanathan Subramanian, Jonas Arnfred, Stefan Winkler
Submitted On:
27 September 2017 - 11:08pm
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poster.pdf

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[1] Vassilios Vonikakis, Ramanathan Subramanian, Jonas Arnfred, Stefan Winkler, "Probabilistic Approach to People-Centric Photo Selection and Sequencing", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2250. Accessed: Oct. 18, 2017.
@article{2250-17,
url = {http://sigport.org/2250},
author = {Vassilios Vonikakis; Ramanathan Subramanian; Jonas Arnfred; Stefan Winkler },
publisher = {IEEE SigPort},
title = {Probabilistic Approach to People-Centric Photo Selection and Sequencing},
year = {2017} }
TY - EJOUR
T1 - Probabilistic Approach to People-Centric Photo Selection and Sequencing
AU - Vassilios Vonikakis; Ramanathan Subramanian; Jonas Arnfred; Stefan Winkler
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2250
ER -
Vassilios Vonikakis, Ramanathan Subramanian, Jonas Arnfred, Stefan Winkler. (2017). Probabilistic Approach to People-Centric Photo Selection and Sequencing. IEEE SigPort. http://sigport.org/2250
Vassilios Vonikakis, Ramanathan Subramanian, Jonas Arnfred, Stefan Winkler, 2017. Probabilistic Approach to People-Centric Photo Selection and Sequencing. Available at: http://sigport.org/2250.
Vassilios Vonikakis, Ramanathan Subramanian, Jonas Arnfred, Stefan Winkler. (2017). "Probabilistic Approach to People-Centric Photo Selection and Sequencing." Web.
1. Vassilios Vonikakis, Ramanathan Subramanian, Jonas Arnfred, Stefan Winkler. Probabilistic Approach to People-Centric Photo Selection and Sequencing [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2250

BAFT: Binary Affine Feature Transform


We introduce BAFT, a fast binary and quasi affine invariant local image feature. It combines the affine invariance of Harris Affine feature descriptors with the speed of binary descriptors such as BRISK and ORB. BAFT derives its speed and precision from sampling local image patches in a pattern that depends on the second moment matrix of the same image patch. This approach results in a fast but discriminative descriptor, especially for image pairs with large perspective changes.

poster.pdf

PDF icon poster.pdf (17 downloads)

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Authors:
Jonas T. Arnfred, Viet Dung Nguyen, Stefan Winkler
Submitted On:
27 September 2017 - 11:05pm
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poster.pdf

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[1] Jonas T. Arnfred, Viet Dung Nguyen, Stefan Winkler, "BAFT: Binary Affine Feature Transform", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2249. Accessed: Oct. 18, 2017.
@article{2249-17,
url = {http://sigport.org/2249},
author = {Jonas T. Arnfred; Viet Dung Nguyen; Stefan Winkler },
publisher = {IEEE SigPort},
title = {BAFT: Binary Affine Feature Transform},
year = {2017} }
TY - EJOUR
T1 - BAFT: Binary Affine Feature Transform
AU - Jonas T. Arnfred; Viet Dung Nguyen; Stefan Winkler
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2249
ER -
Jonas T. Arnfred, Viet Dung Nguyen, Stefan Winkler. (2017). BAFT: Binary Affine Feature Transform. IEEE SigPort. http://sigport.org/2249
Jonas T. Arnfred, Viet Dung Nguyen, Stefan Winkler, 2017. BAFT: Binary Affine Feature Transform. Available at: http://sigport.org/2249.
Jonas T. Arnfred, Viet Dung Nguyen, Stefan Winkler. (2017). "BAFT: Binary Affine Feature Transform." Web.
1. Jonas T. Arnfred, Viet Dung Nguyen, Stefan Winkler. BAFT: Binary Affine Feature Transform [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2249

HIGHLY PARALLEL HEVC MOTION ESTIMATION BASED ON MULTIPLE TEMPORAL PREDICTORS AND NESTED DIAMOND SEARCH


Rate-constrained motion estimation (RCME) is the most computationally intensive task of H.265/HEVC encoding. Massively parallel architectures, such as graphics processing units (GPUs), used in combination with a multi-core central processing unit (CPU), provide a promising computing platform to achieve fast encoding. However, the dependencies in deriving motion vector predictors (MVPs) prevent the parallelization of prediction units (PUs) processing at a frame level.

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Authors:
Esmaeil Hojati, Jean-François Franche, Stéphane Coulombe, Carlos Vázquez
Submitted On:
26 September 2017 - 8:28am
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ICIP2017-Poster-Hojati.pdf

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[1] Esmaeil Hojati, Jean-François Franche, Stéphane Coulombe, Carlos Vázquez, "HIGHLY PARALLEL HEVC MOTION ESTIMATION BASED ON MULTIPLE TEMPORAL PREDICTORS AND NESTED DIAMOND SEARCH", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2247. Accessed: Oct. 18, 2017.
@article{2247-17,
url = {http://sigport.org/2247},
author = {Esmaeil Hojati; Jean-François Franche; Stéphane Coulombe; Carlos Vázquez },
publisher = {IEEE SigPort},
title = {HIGHLY PARALLEL HEVC MOTION ESTIMATION BASED ON MULTIPLE TEMPORAL PREDICTORS AND NESTED DIAMOND SEARCH},
year = {2017} }
TY - EJOUR
T1 - HIGHLY PARALLEL HEVC MOTION ESTIMATION BASED ON MULTIPLE TEMPORAL PREDICTORS AND NESTED DIAMOND SEARCH
AU - Esmaeil Hojati; Jean-François Franche; Stéphane Coulombe; Carlos Vázquez
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2247
ER -
Esmaeil Hojati, Jean-François Franche, Stéphane Coulombe, Carlos Vázquez. (2017). HIGHLY PARALLEL HEVC MOTION ESTIMATION BASED ON MULTIPLE TEMPORAL PREDICTORS AND NESTED DIAMOND SEARCH. IEEE SigPort. http://sigport.org/2247
Esmaeil Hojati, Jean-François Franche, Stéphane Coulombe, Carlos Vázquez, 2017. HIGHLY PARALLEL HEVC MOTION ESTIMATION BASED ON MULTIPLE TEMPORAL PREDICTORS AND NESTED DIAMOND SEARCH. Available at: http://sigport.org/2247.
Esmaeil Hojati, Jean-François Franche, Stéphane Coulombe, Carlos Vázquez. (2017). "HIGHLY PARALLEL HEVC MOTION ESTIMATION BASED ON MULTIPLE TEMPORAL PREDICTORS AND NESTED DIAMOND SEARCH." Web.
1. Esmaeil Hojati, Jean-François Franche, Stéphane Coulombe, Carlos Vázquez. HIGHLY PARALLEL HEVC MOTION ESTIMATION BASED ON MULTIPLE TEMPORAL PREDICTORS AND NESTED DIAMOND SEARCH [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2247

Compressive Image Recovery using Recurrent Generative Model


Reconstruction of signals from compressively sensed measurements is an ill-posed problem. In this paper, we leverage the recurrent generative model, RIDE, as an image prior for compressive image reconstruction. Recurrent networks can model long-range dependencies in images and hence are suitable to handle global multiplexing in reconstruction from compressive imaging. We perform MAP inference with RIDE using back-propagation to the inputs and projected gradient method. We propose an entropy thresholding based approach for preserving texture in images well.

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Authors:
Akshat Dave, Anil Kumar Vadathya, Kaushik Mitra
Submitted On:
26 September 2017 - 7:44am
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icip17_final.pptx

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[1] Akshat Dave, Anil Kumar Vadathya, Kaushik Mitra, "Compressive Image Recovery using Recurrent Generative Model", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2246. Accessed: Oct. 18, 2017.
@article{2246-17,
url = {http://sigport.org/2246},
author = {Akshat Dave; Anil Kumar Vadathya; Kaushik Mitra },
publisher = {IEEE SigPort},
title = {Compressive Image Recovery using Recurrent Generative Model},
year = {2017} }
TY - EJOUR
T1 - Compressive Image Recovery using Recurrent Generative Model
AU - Akshat Dave; Anil Kumar Vadathya; Kaushik Mitra
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2246
ER -
Akshat Dave, Anil Kumar Vadathya, Kaushik Mitra. (2017). Compressive Image Recovery using Recurrent Generative Model. IEEE SigPort. http://sigport.org/2246
Akshat Dave, Anil Kumar Vadathya, Kaushik Mitra, 2017. Compressive Image Recovery using Recurrent Generative Model. Available at: http://sigport.org/2246.
Akshat Dave, Anil Kumar Vadathya, Kaushik Mitra. (2017). "Compressive Image Recovery using Recurrent Generative Model." Web.
1. Akshat Dave, Anil Kumar Vadathya, Kaushik Mitra. Compressive Image Recovery using Recurrent Generative Model [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2246

Plant Leaf Segmentation for Estimating Phenotypic Traits


In this paper we propose a method to segment individual leaves of crop plants from UAV imagery for the purposes of deriving phenotypic properties of the plant. The crop plant used in our study is sorghum Sorghum bicolor (L.) Moench. Phenotyping is a set of methodologies for analyzing and obtaining characteristic traits of a plant. In a phenotypic study, leaves are often used to estimate traits such as individual leaf area and Leaf Area Index (LAI). Our approach is to segment the leaves in polar coordinates using the plant center as the origin.

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Authors:
Yuhao Chen, Christopher Boomsma, Edward Delp
Submitted On:
25 September 2017 - 6:49pm
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poster_icip2017.pdf

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[1] Yuhao Chen, Christopher Boomsma, Edward Delp, "Plant Leaf Segmentation for Estimating Phenotypic Traits", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2245. Accessed: Oct. 18, 2017.
@article{2245-17,
url = {http://sigport.org/2245},
author = {Yuhao Chen; Christopher Boomsma; Edward Delp },
publisher = {IEEE SigPort},
title = {Plant Leaf Segmentation for Estimating Phenotypic Traits},
year = {2017} }
TY - EJOUR
T1 - Plant Leaf Segmentation for Estimating Phenotypic Traits
AU - Yuhao Chen; Christopher Boomsma; Edward Delp
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2245
ER -
Yuhao Chen, Christopher Boomsma, Edward Delp. (2017). Plant Leaf Segmentation for Estimating Phenotypic Traits. IEEE SigPort. http://sigport.org/2245
Yuhao Chen, Christopher Boomsma, Edward Delp, 2017. Plant Leaf Segmentation for Estimating Phenotypic Traits. Available at: http://sigport.org/2245.
Yuhao Chen, Christopher Boomsma, Edward Delp. (2017). "Plant Leaf Segmentation for Estimating Phenotypic Traits." Web.
1. Yuhao Chen, Christopher Boomsma, Edward Delp. Plant Leaf Segmentation for Estimating Phenotypic Traits [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2245

Fitness heart rate measurement using face videos


Recent studies showed that subtle changes in human’s face color due to the heartbeat can be captured by digital video recorders. Most work focused on still/rest cases or those with relatively small motions. In this work, we propose a heart-rate monitoring method for fitness exercise videos. We focus on building a highly precise motion compensation scheme with the help of the optical flow, and use motion information as a cue to adaptively remove ambiguous frequency components for improving the heart rates estimates.

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Authors:
Qiang Zhu, Chau-Wai Wong, Chang-Hong Fu, Min Wu
Submitted On:
4 October 2017 - 9:20am
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Presentation slides (pdf version)

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[1] Qiang Zhu, Chau-Wai Wong, Chang-Hong Fu, Min Wu, "Fitness heart rate measurement using face videos", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2244. Accessed: Oct. 18, 2017.
@article{2244-17,
url = {http://sigport.org/2244},
author = {Qiang Zhu; Chau-Wai Wong; Chang-Hong Fu; Min Wu },
publisher = {IEEE SigPort},
title = {Fitness heart rate measurement using face videos},
year = {2017} }
TY - EJOUR
T1 - Fitness heart rate measurement using face videos
AU - Qiang Zhu; Chau-Wai Wong; Chang-Hong Fu; Min Wu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2244
ER -
Qiang Zhu, Chau-Wai Wong, Chang-Hong Fu, Min Wu. (2017). Fitness heart rate measurement using face videos. IEEE SigPort. http://sigport.org/2244
Qiang Zhu, Chau-Wai Wong, Chang-Hong Fu, Min Wu, 2017. Fitness heart rate measurement using face videos. Available at: http://sigport.org/2244.
Qiang Zhu, Chau-Wai Wong, Chang-Hong Fu, Min Wu. (2017). "Fitness heart rate measurement using face videos." Web.
1. Qiang Zhu, Chau-Wai Wong, Chang-Hong Fu, Min Wu. Fitness heart rate measurement using face videos [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2244

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