Conferences related to Optimal Transport

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2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE


2020 59th IEEE Conference on Decision and Control (CDC)

The CDC is the premier conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2020 IEEE International Conference on Plasma Science (ICOPS)

IEEE International Conference on Plasma Science (ICOPS) is an annual conference coordinated by the Plasma Science and Application Committee (PSAC) of the IEEE Nuclear & Plasma Sciences Society.


2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.


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Periodicals related to Optimal Transport

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Antennas and Propagation, IEEE Transactions on

Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.


Applied Superconductivity, IEEE Transactions on

Contains articles on the applications and other relevant technology. Electronic applications include analog and digital circuits employing thin films and active devices such as Josephson junctions. Power applications include magnet design as well asmotors, generators, and power transmission


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Communications Letters, IEEE

Covers topics in the scope of IEEE Transactions on Communications but in the form of very brief publication (maximum of 6column lengths, including all diagrams and tables.)


Communications Magazine, IEEE

IEEE Communications Magazine was the number three most-cited journal in telecommunications and the number eighteen cited journal in electrical and electronics engineering in 2004, according to the annual Journal Citation Report (2004 edition) published by the Institute for Scientific Information. Read more at http://www.ieee.org/products/citations.html. This magazine covers all areas of communications such as lightwave telecommunications, high-speed data communications, personal communications ...


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Xplore Articles related to Optimal Transport

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High Frequency Ultrasonic Tomography Using Optimal Transport Distance

2018 IEEE International Ultrasonics Symposium (IUS), 2018

Nonlinear tomographic ultrasound reconstructions can provide quantitative data about material parameters like the speed of sound also in reflection-mode imaging. However, these methods suffer from the cycle skipping issue which is caused by the high center frequencies usual in medical ultrasound hardware. As these algorithms are based on minimization of a misfit function where the high frequency signals lead to ...


Population Averaging of Neuroimaging Data Using L<sup>p</sup>Distance-based Optimal Transport

2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI), 2018

Analyzing neuroimaging data at the population level relies on averaging images that have been acquired on a group of individuals drawn from this population. Traditional group analyses are based on the general linear model, which performs euclidean averaging across individuals, independently at each brain location. It is therefore largely impacted by interindividual differences. In this paper we propose to overcome ...


Informed secure watermarking using optimal transport

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011

This paper presents several watermarking methods preventing the estimation of the secret key by an adversary. The constraints for secure embedding using distribution matching, where the decoding regions rely implicitly on the distribution of the host signal, are first formulated. In order to perform informed coding, different decoding regions are associated with the same message using an appropriate partitioning function. ...


Optimal transport using Helmholtz-Hodge decomposition and first-order primal-dual algorithms

2015 IEEE International Conference on Image Processing (ICIP), 2015

This work deals with the resolution of the optimal transport problem between 2D images in the fluid mechanics framework of Benamou and Brenier formulation [1], which numerical resolution is still challenging even for medium-sized images. We develop a method using the Helmholtz-Hodge decomposition [2] in order to enforce the divergence-free constraint throughout the iterations. We then show how to use ...


Optimal transport for data fusion in remote sensing

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016

One of the main objective of data fusion is the integration of several acquisition of the same physical object, in order to build a new consistent representation that embeds all the information from the different modalities. In this paper, we propose the use of optimal transport theory as a powerful mean of establishing correspondences between the modalities. After reviewing important ...


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Educational Resources on Optimal Transport

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IEEE-USA E-Books

  • High Frequency Ultrasonic Tomography Using Optimal Transport Distance

    Nonlinear tomographic ultrasound reconstructions can provide quantitative data about material parameters like the speed of sound also in reflection-mode imaging. However, these methods suffer from the cycle skipping issue which is caused by the high center frequencies usual in medical ultrasound hardware. As these algorithms are based on minimization of a misfit function where the high frequency signals lead to local minima, false reconstruction are the result. Previous approaches try to circumvent this problem by low pass filtering the measured signals. This method is limited by the bandwidth and SNR of the measurement system. In this contribution, we applied the optimal transport distance known from seismic reconstructions. This approach uses all frequency components while enhancing the included traveltime information. We compare the misfit functions and show reconstructions using the standard and the optimal transport method. The results demonstrate an at least 1.6 times higher convergence limit.

  • Population Averaging of Neuroimaging Data Using L<sup>p</sup>Distance-based Optimal Transport

    Analyzing neuroimaging data at the population level relies on averaging images that have been acquired on a group of individuals drawn from this population. Traditional group analyses are based on the general linear model, which performs euclidean averaging across individuals, independently at each brain location. It is therefore largely impacted by interindividual differences. In this paper we propose to overcome this variability by using optimal transport to leverage the geometrical properties of multivariate brain patterns. We extend the concept of Wasserstein barycenter, which was initially meant to average probability measures, to make it applicable to arbitrary data that do not necessarily fulfill the properties of a true probability measure. For this, we introduce a new algorithm that estimates a barycenter using the transportation Lpdistance [8]. We provide an experimental study on how the noise level impacts the quality of the obtained barycenter on artificial data. Our proposed method is compared with the approach introduced in [6] on artificial and real functional MRI.

  • Informed secure watermarking using optimal transport

    This paper presents several watermarking methods preventing the estimation of the secret key by an adversary. The constraints for secure embedding using distribution matching, where the decoding regions rely implicitly on the distribution of the host signal, are first formulated. In order to perform informed coding, different decoding regions are associated with the same message using an appropriate partitioning function. The minimization of the embedding distortion is afterwards casted into an optimal transport problem. Three new secure embeddings are presented and the performances of the proposed embedding functions regarding the AWGN channel for different WCRs are evaluated. Depending on the embedding and noise distortions, informed secure coding can outperform classical secure coding or classical insecure coding such as ISS or SCS.

  • Optimal transport using Helmholtz-Hodge decomposition and first-order primal-dual algorithms

    This work deals with the resolution of the optimal transport problem between 2D images in the fluid mechanics framework of Benamou and Brenier formulation [1], which numerical resolution is still challenging even for medium-sized images. We develop a method using the Helmholtz-Hodge decomposition [2] in order to enforce the divergence-free constraint throughout the iterations. We then show how to use a first order primal-dual algorithm for convex problems of Chambolle and Pock [3] to solve the obtained problem, leading to a new algorithm easy to implement. Besides, numerical experiments demonstrate that this algorithm is faster than state of the art methods and efficient with real-sized images.

  • Optimal transport for data fusion in remote sensing

    One of the main objective of data fusion is the integration of several acquisition of the same physical object, in order to build a new consistent representation that embeds all the information from the different modalities. In this paper, we propose the use of optimal transport theory as a powerful mean of establishing correspondences between the modalities. After reviewing important properties and computational aspects, we showcase its application to three remote sensing fusion problems: domain adaptation, time series averaging and change detection in LIDAR data.

  • Numerical simulation of optimal transport paths

    This article has been retracted by the publisher.

  • Adaptive color transfer with relaxed optimal transport

    This paper studies the problem of color transfer between images using optimal transport techniques. While being a generic framework to handle statistics properly, it is also known to be sensitive to noise and outliers, and is not suitable for direct application to images without additional postprocessing regularization to remove artifacts. To tackle these issues, we propose to directly deal with the regularity of the transport map and the spatial consistency of the reconstruction. Our approach is based on the relaxed and regularized discrete optimal transport method of [1]. We extend this work by (i) modeling the spatial distribution of colors within the image domain and (ii) tuning automatically the relaxation parameters. Experiments on real images demonstrate the capacity of our model to adapt itself to the considered data.

  • Unsupervised Domain Adaptation with Regularized Optimal Transport for Multimodal 2D+3D Facial Expression Recognition

    Since human expressions have strong flexibility and personality, subject- independent facial expression recognition is a typical data bias problem. To address this problem, we propose a novel approach, namely unsupervised domain adaptation with regularized optimal transport for multimodal 2D+3D Facial Expression Recognition (FER). In particular, Wasserstein distance is employed to measure the distribution inconsistency between the training samples (i.e. source domain) and test samples (i.e. target domain). Minimization of this Wasserstein distance is equivalent to finding an optimal transport mapping from training to test samples. Once we find this mapping, original training samples can be transformed into a new space in which the distributions of the mapped training samples and the test samples can be well-aligned. In this case, classifier learned from the transformed training samples can be well generalized to the test samples for expression prediction. In practice, approximate optimal transport can be effectively solved by adding entropy regularization. To fully explore the class label information of training samples, group sparsity regularizer is also used to enforce that the training samples from the same expression class can be mapped to the same group. Experimental results evaluated on the BU-3DFE and Bosphorus databases demonstrate that the proposed approach can achieve superior performance compared with the state-of-the-art methods.

  • Computational Optimal Transport: With Applications to Data Science

    The goal of Optimal Transport (OT) is to define geometric tools that are useful to compare probability distributions. Their use dates back to 1781. Recent years have witnessed a new revolution in the spread of OT, thanks to the emergence of approximate solvers that can scale to sizes and dimensions that are relevant to data sciences. Thanks to this newfound scalability, OT is being increasingly used to unlock various problems in imaging sciences (such as color or texture processing), computer vision and graphics (for shape manipulation) or machine learning (for regression, classification and density fitting). This monograph reviews OT with a bias toward numerical methods and their applications in data sciences, and sheds lights on the theoretical properties of OT that make it particularly useful for some of these applications. Computational Optimal Transport presents an overview of the main theoretical insights that support the practical effectiveness of OT before explaining how to turn these insights into fast computational schemes. Written for readers at all levels, the authors provide descriptions of foundational theory at two-levels. Generally accessible to all readers, more advanced readers can read the specially identified more general mathematical expositions of optimal transport tailored for discrete measures. Furthermore, several chapters deal with the interplay between continuous and discrete measures, and are thus targeting a more mathematically-inclined audience. This monograph will be a valuable reference for researchers and students wishing to get a thorough understanding of Computational Optimal Transport, a mathematical gem at the interface of probability, analysis and optimization.

  • An Optimal Transport-Based Approach for Crowd Evacuation

    The effectiveness of crowd evacuation is an important public safety issue. Crowd related incidents are frequent and often result in serious material and human loss, hence the increasing interest in developing new techniques to assist in crowd evacuation. In this paper, we introduce a new crowd evacuation method, where the evacuation plan is computed based on optimal transport. The use of the optimal transport formulation results in the minimization of the kinetic energy necessary to perform the evacuation, thus reducing the effort exercised by the crowd members. To ensure the safety and the feasibility of the computed plan, a safety model is introduced as an additional factor in the optimization. The proposed model allows avoiding dangerous places, spreading out the crowd, limiting the crowd density and imposing one-way circulation. A numerical method adapted to the resulting optimization problem is presented. The efficiency of the proposed approach is evaluated through simulation. The experimental results show that the proposed method computes a solution that strikes a balance between the different considered factors.



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