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<p>A shape matching technique based on the straight line Hough transform (SLHT) is presented. In the theta - rho 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 theta - rho space by computing the distances between pairs of points having the same theta 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 theta - rho space to the x-y plane.</p>
scalable translation invariant rotation to shifting signature; image recognition; straight line Hough transform; shape matching; shape signature; image space; 1D correlation; inverse transform; Hough transforms; image recognition

D. Pao, H. Li and R. Jayakumar, "Shapes Recognition Using the Straight Line Hough Transform: Theory and Generalization," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 14, no. , pp. 1076-1089, 1992.
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