Course
description
The
last
decade has seen a tremendous interest in
gradient
domain manipulation techniques for
applications in vision and graphics
including
retinex, high dynamic range (HDR) tone
mapping, image fusion (mosaics),
image
editing, image matting, video synthesis,
texture de-emphasis and 3D
mesh
editing. These techniques manipulate gradients
of single image/multiple
images/video/surfaces and reconstruct
images/video/surfaces from the
manipulated
gradient fields. Reconstruction from gradient
fields, itself, has a
long
history in computer vision, going back to the
work in Photometric
Stereo, Shape
from Shading and brightness constancy.
In
this course, we address the theoretical
aspects of curl,
divergence and integrability of vector fields,
relevant to vision and
graphics
problems. We discuss scenarios where it is
beneficial to operate on
gradients
than image intensities for image
understanding, manipulation and
synthesis. We
review gradient domain techniques; address
issues involved in 2D and 3D
reconstructions from gradients, discuss
implementations/numerical
methods and give
in-depth technical insight into the modern
applications that exploit
gradient
domain manipulations.
The
participants
will learn about topics for extracting
scene properties for computer vision as well
as image manipulation
methods for
generating compelling pictures for computer
graphics, with several
examples. We
hope to provide enough fundamentals to satisfy
the technical specialist
as well
as tools/software’s to aid graphics and vision
researchers, including
graduate
students.
Course Outline
Bibliography (html)
Pseudo-Code for gradient integration
C and Matlab Codes for solving Poisson equation, gradient domain edge suppression, robust reconstruction from gradient fields
Slides: Download from link
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