Detection, Rectification and Segmentation of Co-planar Repeated Patterns

James Pritts (CTU Prague, Czech Republic)

Abstract:

We present a novel and general method for detection, rectification and segmentation of co-planar repeated patterns imaged. The only assumption on the image content is that repeated elements of the pattern can be mapped to each other in the scene plane by a set of Euclidean transformations. This is a very general assumption that covers nearly all commonly seen man-made repetitive patterns.

In addition, novel linear constraints are exploited that enable geometric ambiguity reduction between the rectification of the imaged pattern and the real-world pattern. The remaining ambiguity is within a similarity if the scene plane contains repeated elements that are rotated differently, or within a similarity with a scale ambiguity along the axis of symmetry if any of the elements are reflected. The method is successfully tested on a broad range of image types including those where state-of-the-art methods fail.

Joint work with Ondra Chum and Jiri Matas