Digital Image Processing
Pattern Recognition and Image Analysis
| Hi-index | 0.00 |
In the recursive estimate of parameters of interframe geometric deformations of digital images, calculating the pseudogradient from a local sample and current estimates of deformation parameters to be measured involves finding derivatives in terms of finite differences. The maximal convergence rate of estimates of the deformation parameters can be achieved when using the most informative readings of images in a local sample. Under this approach, there exist optimal values of increments in the parameters and basic axes, depending on the mismatch between estimates and correlation properties of the image. The performance of the procedures may be increased if the increments are optimized at each iteration. The potential accuracy of estimation of the parameters, as given by the Cramer-Rao inequality, serves as an optimality criterion when calculating the pseudogradient of the objective function. It is shown that the condition of maximum of information can be reduced to the condition of maximization of the ratio of the expectation of the pseudogradient to its mean square deviation. Application of optimized values of increments enables one to reduce considerably (up to several times) the computational cost with the same accuracy of estimation.