A New Workspace For Principal Axes And Scaling Estimation

A novel algorithm for 2D object orientation and scaling factor estimation, is proposed in this paper. The proposed method is accurate, effective, computationally efficient and fullyautomated. The object orientation is calculated by using object principal axes estimation. The main contribution of the proposed approach is the utilization of a 2D empirical mode like decomposition (EMD-like) as a new workspace for principal axes and scaling determination. The EMD algorithm can decompose any nonlinear and non-stationary data into a number of intrinsic mode functions (IMFs). When the object is decomposed by empirical mode like decomposition (EMD-like), the IMFs of the object, provide a workspace with very good properties for calculating the principal axes. The method was evaluated on synthetic and real images. The experimental results demonstrate the effectiveness and the accuracy of the method, both in orientation and scaling estimations. keywords: Principal axes estimation, orientation estimation, scaling estimation, empirical mode decomposition, ensemble empirical mode decomposition, intrinsic mode.