The summer school will involve leaders from Computer Vision and experts from Mathematics, Statistics, Engineering and Computer Science who are interested in Vision. Computer Vision is a rapidly developing interdisciplinary field with an increasing number of practical applications such as automated cars, visual surveillance, and aids for the visually impaired. Its main goal is the automatic understanding and interpreting of images and image sequences. The school will present the core techniques in Computer Vision, illustrate the large range of visual tasks they can be applied to, and describe the conceptual and theoretical foundations that underlie them. These techniques include filtering, geometry, differential equations, harmonic analysis, probabilistic methods, machine learning, and many more. The school will describe real world applications and discuss interactions with related disciplines such as image processing, machine learning, and biological vision.
(Johns Hopkins University, Applied Mathematics and Statistics)
Fei Fei Li (Stanford University)
Deva Ramanan (University of California, Irvine (UCI))
Stefano Soatto (University of California, Los Angeles (UCLA), Computer Science)
Zhuowen Tu (University of California, Los Angeles (UCLA), School of Medicine)
Alan Yuille (University of California, Los Angeles (UCLA), Psychology)