Detection and estimation translations of large images using random projections

This paper describes a technique of fast detection and estimation of translations in large image frames for image stabilization. Our approach is based on random projection methodology for reduction of image dimension which retain with prescribed accuracy and probability the squared Euclidean norm of projected vectors (image's rows and columns). We formulate simple optimization problems for estimation of vertical and horizontal motions of the frame. The first one tries to find the best match between energies (squared norms) of rows (columns) of the actual projected image and the projected reference frame. The second one uses the squared Euclidean distance between the same objects. An iterative procedure based on the successive, alternate estimation of a vertical and a horizontal translation is proposed.