Multi-Image Matching Using Multi-Scale Oriented Patches

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 samp...

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Matthew Brown, Richard Szeliski, Simon A. J. Winder
• Keywords: Computer Vision, CVPR 2005, Feature Descriptor, Incorrect Feature, Invariant Feature, Nearest Neighbour Algorithm, Pairwise Feature Match