An Interactive Approach to Solving Correspondence Problems

作者:Stefanie Jegelka, Ashish Kapoor, Eric Horvitz

摘要

Finding correspondences among objects in different images is a critical problem in computer vision. Even good correspondence procedures can fail, however, when faced with deformations, occlusions, and differences in lighting and zoom levels across images. We present a methodology for augmenting correspondence matching algorithms with a means for triaging the focus of attention and effort in assisting the automated matching. For guiding the mix of human and automated initiatives, we introduce a measure of the expected value of resolving correspondence uncertainties. We explore the value of the approach with experiments on benchmark data.

论文关键词:Human interaction, Active learning, Value of information, Matching, Correspondence problems

论文评审过程:

论文官网地址:https://doi.org/10.1007/s11263-013-0657-5