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