Orthogonal projection transform with application to shape description

In this paper, an approach called orthogonal projection transform (OPT) is proposed for invariant shape description. It is inspired by the construction of orthogonal Fourier-Mellin moments (OFMMs), but integral is only performed along lines with different polar angles in the proposed approach. By performing OPT, any object can be converted to a set of closed curves. In comparison with moment based methods, such as OFMMs, complex moments (CMs) etc., these projected curves not only preserve the information along lines with different polar angles, but also derive the information of the global intensity distributions. Descriptors, which are invariant to translation, rotation and scaling, are constructed from these closed curves. Applying the descriptors to images taken from standard image datasets, the numerical experiments show that the proposed method is more robust than some moment based methods such as OFMMs and CMs to noise, occlusions and real view angle disturbances.