Geometric Grouping of Repeated Elements within Images

The objective of this work is the automatic detection and grouping of imaged elements which repeat in a scene. We show that structures that repeat in the world (for example wall paper patterns) are related by particular parametrized transformations in perspective images. These image transformations provide powerful grouping constraints, and can be used at the heart of hypothesize and verify grouping algorithms. Parametrized transformations are given for some classes of repeating operation in the world as well as some groupers based on these. These groupers are demonstrated on a number of real images, where both the elements and the grouping are determined automatically. It is also shown that the repeating element can be learnt from the image, and hence provides an image descriptor.

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