ICERM Semester Program on "Computer Vision"
(February 4 - May 10, 2019)

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Introduction

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.

Problems that will be considered in the program include image restoration, image segmentation, object recognition and 3D reconstruction. Current approaches to address these problems draw on a variety of mathematical and computational topics such as stochastic models, statistical methods, differential geometry, signal processing, numerical algorithms and combinatorial optimization. Practical considerations also require the use of a wide variety of computational methods, including techniques that scale to large datasets.

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.

Organizing Committee

  • Yali Amit
    (University of Chicago)
  • Ronen Basri
    (Weizmann Institute)
  • Alex Berg
    (UNC Chapel Hill)
  • Tamara Berg
    (UNC Chapel Hill)
  • Pedro Felzenszwalb
    (Brown University)
  • Stuart Geman
    (Brown University)
  • Basilis Gidas
    (Brown University)
  • David Jacobs
    (University of Maryland)
  • Benar Svaiter
    (Instituto de Matematica Pura e Applicada (IMPA))
  • Olga Veksler
    (University of Western Ontario)