Multi-Image Matching Using Multi-Scale Oriented Patches
Brown, M., Szeliski, R. and Winder, S., 2005. Multi-Image Matching Using Multi-Scale Oriented Patches. In: CVPR 2005: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, 2005-06-20 - 2005-06-25, San Diego.
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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.
|Item Type||Conference or Workshop Items (Paper)|
|Creators||Brown, M., Szeliski, R. and Winder, S.|
|Departments||Faculty of Science > Computer Science|
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