Using Local Affine Invariants to Improve Image Matching

Abstract

A method to classify tentative feature matches as inliers or outliers to a transformation model is presented. It is well known that ratios of areas of corresponding shapes are affine invariants. Our algorithm uses consistency of ratios of areas in pairs of images to classify matches as inliers or outliers. The method selects four matches within a region, and generates all possible corresponding triangles. All matches are classified as inliers or outliers based on the variance among the ratio of areas of the triangles. The selected inliers are used to compute a homography transformation. We present experimental results showing significant improvements over the baseline RANSAC algorithm for pairs of images from the Zurich Building Database.