Meetings/Workshops on Applied Maths: Pattern Recognition and Image Processing, Machine Vision in the United States (USA)

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Computer Vision (Semester Program)
04 Feb 2019 - 10 May 2019 • ICERM/Brown University, Providence, United States
Computer vision is an inter-disciplinary topic crossing boundaries between computer science, statistics, mathematics, engineering, and cognitive science. Research in computer vision involves the development and evaluation of computational methods for image analysis. This includes the design of new theoretical models and algorithms, and practical implementation of these algorithms using a variety of computer architectures and programming languages. The methods under consideration are often motivated by generative mathematical models of the world and the imaging process. Recent approaches also rely heavily on machine learning techniques and discriminative models such as deep neural networks. The focus of the program will be on problems that involve modeling, machine learning, and optimization. The program will also bridge a gap between theoretical approaches and practical algorithms, involving researchers with a variety of backgrounds.
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Computational Imaging
18 Mar 2019 - 22 Mar 2019 • ICERM, Brown University, Providence, United States
Computational imaging involves the use of mathematical models and computational methods as part of imaging systems. Algorithms for image reconstruction have important applications, including in medical image analysis and imaging for the physical sciences. Classical approaches often involve solving large inverse problems using a variety of regularization methods and numerical algorithms.
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Workshop I: Geometric Processing
01 Apr 2019 - 05 Apr 2019 • Institute for Pure and Applied Mathematics (IPAM),, United States
Over the last few decades, it has become common to treat data as samples belonging to geometric manifolds or more general nonlinear metric spaces. Together with increasing computer power, this has opened the way to new acquisition and representation methods, and to new data processing techniques; leading to very challenging theoretical and practical questions which require an interplay between differential and metric geometries, optimization, PDEs, stochastic analysis, and computer science. This workshop aims to bring together leading experts in these fields and young researchers to exchange ideas, create synergies,and enhance current and outline future directions of research.
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Automate 2019
08 Apr 2019 - 11 Apr 2019 • Chicago, IL, United States
Automation is helping companies in every industry become stronger global competitors. To succeed, you need the right solution providers, the right technology, and the right expertise. Automate 2019 will provide it all and more! Automate showcases the full spectrum of automation technologies and solutions. From traditional industrial applications to cutting edge new technologies, you'll find ways to automate every process in your company. It's held just once every two years, so be sure to take advantage of this opportunity to find the right solutions to meet your automation challenges, all at this one great show.
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Shape Analysis
15 Apr 2019 - 19 Apr 2019 • Institute for Pure and Applied Mathematics (IPAM),, United States
Fast acquisition technology and broad availability of 3D data underscore the need for advanced tools that process and analyze 3D shapes. Unlike image and signal processing which handle functions on flat domains with well-developed tools for processing and learning, 3D shapes present unique challenges due to their irregular and weak structure. Despite breakneck progress in the development of tools for these tasks, many challenges remain in automatically analyzing, processing, and understanding 3D geometry. In particular, recent advances in machine learning have shown advancement in signal and image processing, while the processing of 3D shapes is less developed. This workshop aims to bring world-leading researchers in mathematics and computer science to study, explore, collaborate, and develop new ideas and research directions in combining traditional 3D shape analysis with recent developments of learning.
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Computer Vision Conference (CVC) 2019
25 Apr 2019 - 26 Apr 2019 • Las Vegas, Nevada, United States
Computer vision is the science and technology of making machines that see. It is concerned with the theory, design and implementation in the field of machine vision, image processing, data science and pattern recognition. The participants will benefit from direct interaction and discussions with world leaders in Computer Vision. Participants will also have the possibility to present the results of their research, and to interact with their scientific peers, in a friendly and constructive environment.
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Data Science and Image Analysis Conference of the Pacific Northwest
27 Apr 2019 - 28 Apr 2019 • Eastern Washington University, Cheney, United States
Eastern Washington University, Washington State University, and Gonzaga University will co-host the Data Science and Image Analysis Conference of the Pacific Northwest on April 27 and 28, 2019, at Eastern Washington University in Cheney, Washington. The first day will have professionals from industry, government, and academia in the fields of data science and image analysis come together, along with students and post-doctoral researchers, to collaborate on open problems through short talks, in-depth group discussions, and breakout sessions. The second day will consist of professional development activities for students and junior researchers, and will feature presentations from members of this group, with direct feedback provided from professionals. Workshops will be offered as a practical introduction to these fields, and panel discussions will provide timely career advice. Regional recruiters will also be present from industry and Pacific Northwest National Laboratory.
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Mathematics in Optical Imaging
29 Apr 2019 - 03 May 2019 • Institute for Mathematics and its Applications, Un, United States
This one week workshop aims to disseminate recent advancements from various areas and bridge different research fields (experimentalists and theorists) for an integrated and thorough understanding of optical imaging. Even with the enhanced progress from past decades, there are a large amount of theoretical studies waiting to be made applicable, with plenty of experimental results to be mathematically interpreted. This workshop will provide a platform for researchers across disciplines to communicate, discuss, and exchange ideas under the common theme of optical imaging.
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Optimization Methods in Computer Vision and Image Processing
29 Apr 2019 - 03 May 2019 • ICERM, Brown University, Providence, United States
Optimization appears in many computer vision and image processing problems such as image restoration (denoising, inpainting, compressed sensing), multi-view reconstruction, shape from X, object detection, image segmentation, optical flow, matching, and network training. While there are formulations allowing for global optimal optimization, e.g. using convex objectives or exact combinatorial algorithms, many problems in computer vision and image processing require efficient approximation methods. Optimization methods that are widely used range from graph-based techniques and convex relaxations to greedy approaches (e.g. gradient descent). Each method has different efficiency and optimality guarantees. The goal of this workshop is a broad discussion of mathematical models (objectives and constraints) and robust efficient optimization methods (exact or approximate, discrete or continuous) addressing existing issues and advancing the state of the art.
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Workshop III: Geometry of Big Data
06 May 2019 - 10 May 2019 • Institute for Pure and Applied Mathematics (IPAM),, United States
Exploring, understanding and utilizing geometric structures of big data can be of crucial importance in data analysis and machine learning algorithms. For example, the set of image patches or 3D surfaces usually stays near a low dimensional manifold. This manifold structure can be used to efficiently characterize similarities and dissimilarities. It is also desirable to design features that are invariant under certain transformations or group actions. When these features are used as input or desired properties are incorporated into learning structures and algorithms, the accuracy, efficiency, and interpretability of the whole process is significantly enhanced. In this workshop, we aim to investigate and study the possibilities and potential of the integration of geometry, modeling, and learning from theory and principle to practice and implementation in order to take advantage of both model-based and learning-based approaches.
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msmlc — 2nd Midwest Statistical Machine Learning Colloquium
13 May 2019 • Ames, United States
This one-day regional meeting of statisticians, engineers, computer scientists, mathematicians, and practitioners interested in the theory and applications of Statistical Machine Learning is being organized by the ISU Departments of Industrial & Manufacturing Systems Engineering and of Statistics. The purpose of the meeting is to bring together a diverse set of researchers and practitioners from multiple disciplines to discuss current motivating problems and methodological advances in theory-based machine learning.
Phone: [5152405254];     Email:
machine learning, statistics, applications, data analytics
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CVPR 2019 — IEEE Conference on Computer Vision and Pattern Recognition
15 Jun 2019 - 21 Jun 2019 • Long Beach, CA, United States
CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.
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Workshop — Deep Learning and Medical Applications
27 Jan 2020 - 31 Jan 2020 • Los Angeles, CA, United States
Institute for Pure and Applied Mathematics (IPAM), UCLA
Rapid advances in deep learning techniques are starting to revolutionize medical imaging. Radiology, disease detection, and tissue imaging are all expected to be facilitated by automated image analysis programs in the near future. Many new interdisciplinary research questions arise; finding solutions with practical significance requires input from mathematicians, bio-physicists, and computational engineers. This workshop aims to bring together researchers from different backgrounds to explore this new frontier of science.
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AIP Conference Proceedings
Last updated: 07 February 2019