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Matching Maximally Stable Extremal Regions Using Edge Information and the Chamfer Distance Function

We consider the problem of image recognition using local features. We present a method for matching Maximally Stable Extremal Regions using edge information and the chamfer distance function. We represent MSERs using the Canny edges of their binary image representation in an affine normalized coordinate frame and find correspondences using chamfer matching. We evaluate the performance of our approach on a large number of data sets commonly used in the computer vision literature and we show that it is useful for matching images under large affine and viewpoint transformations as well as blurring, illumination changes and JPEG compression artifacts.