Improved Video Registration using Non-Distinctive Local Image Features

The task of registering video frames with a static model is a common problem in many computer vision domains. The standard approach to registration involves finding point correspondences between the video and the model and using those correspondences to numerically determine registration transforms. Current methods locate video-to-model point correspondences by assembling a set of reference images to represent the model and then detecting and matching invariant local image features between the video frames and the set of reference images. These methods work well when all video frames can be guaranteed to contain a sufficient number of distinctive visual features. However, as we demonstrate, these methods are prone to severe misregistration errors in domains where many video frames lack distinctive image features. To overcome these errors, we introduce a concept of local distinctiveness which allows us to find model matches for nearly all video features, regardless of their distinctive...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Robin Hess, Alan Fern
• Keywords: Computer Vision, CVPR 2007, Distinctive Image Features, Distinctive Visual Features, Registration Methods, Video Frames, Video-to-model Point Correspondences