2002/11/07 by Adam Baumberg · 1 citation
Engineering · Computer Science · Mathematics · #Robotics and Sensor-Based Localization #Advanced Vision and Imaging #Advanced Image and Video Retrieval Techniques #Artificial intelligence #Computer science #Computer vision #Affine transformation #Feature (linguistics) #Matching (statistics) #Pattern recognition (psychology) #Invariant (physics) #Feature extraction #Skew #Feature matching #Mathematics
paper · doi:10.1109/cvpr.2000.855899
openalex publication_date 2002/11/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
We present a robust method for automatically matching features in images corresponding to the same physical point on an object seen from two arbitrary viewpoints. Unlike conventional stereo matching approaches we assume no prior knowledge about the relative camera positions and orientations. In fact in our application this is the information we wish to determine from the image feature matches. Features are detected in two or more images and characterised using affine texture invariants. The problem of window effects is explicitly addressed by our method-our feature characterisation is invariant to linear transformations of the image data including rotation, stretch and skew. The feature matching process is optimised for a structure-from-motion application where we wish to ignore unreliable matches at the expense of reducing the number of feature matches.