Wide-baseline image change detection
We present a fully automated method for the detection of changes within a scene between a reference and a sample image whose viewing angles differ by up to 30°. We also describe an extension to the SIFT technique that allows extracted feature points to be matched over wider viewing angles. Matched correspondences between reference and sample images are used to construct a Delaunay triangulation and changes are detected by comparing triangles after affine compensation using a dense SIFT metric. False positives are reduced by using a novel technique introduced as local plane matching (LPM) to match mean-shift segments in unmatched areas using the homographies of local planes to compensate for perspective distortions. The method is shown to achieve pixel-level equal error rates of 5% at a 10° azimuth view angle difference.