This paper describes a novel multi-view matching framework based on a new type of invariant feature. Our features are located at Harris corners in discrete scale-space and oriented using a blurred local gradient. This defines a rotationally invariant frame in which we sample a feature descriptor, which consists of an 8 × 8 patch of bias/gain normalised intensity values. The density of features in the image is controlled using a novel adaptive non-maximal suppression algorithm, which gives a better spatial distribution of features than previous approaches. Matching is achieved using a fast nearest neighbour algorithm that indexes features based on their low frequency Haar wavelet coefficients. We also introduce a novel outlier rejection procedure that verifies a pairwise feature match based on a background distribution of incorrect feature matches. Feature matches are refined using RANSAC and used in an automatic 2D panorama stitcher that has been extensively tested on hundreds of sample inputs.
|Number of pages
|Published - Jun 2005
|CVPR 2005: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005 - San Diego
Duration: 20 Jun 2005 → 25 Jun 2005
|CVPR 2005: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005
|20/06/05 → 25/06/05