Session 2B Keynote

A dark secret in the computer vision and image processing research community is the heavy reliance on "magic numbers" by our algorithms. Magic numbers often manifest themselves as algorithmic parameters that must be tuned before satisfactory results can be obtained. This over reliance on parameter tuning stems, in part, from the long-standing dogma that computer vision algorithms should be fully automated. Interestingly, requiring the user to tune parameters, most of which have no intuitive meaning to the task at hand, is far from automatic in fact, it is a major stumbling block when building real world computer vision applications. In this talk, I will advocate that for many computer vision and image processing applications magic numbers can be avoided if we instead exploit the user\´s help via meaningful interaction. This approach to solving problems has been termed "interactive computer vision" and has proven effective in many tasks such as segmentation, matting, and image repair. Specifically, I\´ll discuss several examples from our own research that have transformed problems either too difficult to automate or heavily reliant on parameter tuning into applications that now rely only on simple, and easy to understand, interaction supplied by the user.