Shapes recognition using the straight line Hough transform: theory and generalization

A shape matching technique based on the straight line Hough transform (SLHT) is presented. In the θ-ρ space, the transform can be expressed as the sum of the translation term and the intrinsic term. This formulation allows the translation, rotation, and intrinsic parameters of the curve to be easily decoupled. A shape signature, called the scalable translation invariant rotation-to-shifting (STIRS) signature, is obtained from the θ-ρ space by computing the distances between pairs of points having the same θ value. This signature is invariant to translation and can be easily normalized, and rotation in the image space corresponds to circular shifting of the signature. Matching two signatures only amounts to computing a 1D correlation. The height and location of a peak (if it exists) indicate the similarity and orientation of the test object with respect to the reference object. The location of the test object is obtained, once the orientation is known, by an inverse transform (voting) from the θ-ρ space to the x-y plane