Affine invariant shape projection distribution for shape matching using relaxation labelling

Abstract

Shape is considered to be one of the most promising tools to represent and recognise an object. In this study, an effective and rigorous shape matching algorithm is developed based on a new descriptor and relaxation labelling technique. For each contour point, the descriptor captures the distribution of all points within the shape region along the vector perpendicular to that from the centroid to the point. In addition to stable affine invariance, the descriptor is robust to noise since it makes use of all points in the shape region. The descriptor distance is used to initialise the contour point matching probability, and relaxation labelling technique is utilised to update the matching probability using a new compatibility coefficient function, which is defined based on the shape projection preserving characteristic. The experiments on synthetic and real remote sensing data are provided to test the performance of the authors’ proposed algorithm. Compared to other four state‐of‐the‐art contour‐based shape matching algorithms, their algorithm is more robust and capable of shape matching under affine transformations and noise.

Keywords

• affine invariant shape projection distribution
• shape matching
• relaxation labelling
• affine invariance
• descriptor distance
• contour point matching probability