  This paper deals with area-based subpixel image registration under
rotation-isometric scaling-translation transformation hypothesis. Our approach
is based on a parametrical modeling of geometrically transformed textural image
fragments and maximum likelihood estimation of transformation vector between
them. Due to the parametrical approach based on the fractional Brownian motion
modeling of the local fragments texture, the proposed estimator MLfBm (ML
stands for "Maximum Likelihood" and fBm for "Fractal Brownian motion") has the
ability to better adapt to real image texture content compared to other methods
relying on universal similarity measures like mutual information or normalized
correlation. The main benefits are observed when assumptions underlying the fBm
model are fully satisfied, e.g. for isotropic normally distributed textures
with stationary increments. Experiments on both simulated and real images and
for high and weak correlation between registered images show that the MLfBm
estimator offers significant improvement compared to other state-of-the-art
methods. It reduces translation vector, rotation angle and scaling factor
estimation errors by a factor of about 1.75...2 and it decreases probability of
false match by up to 5 times. Besides, an accurate confidence interval for
MLfBm estimates can be obtained from the Cramer-Rao lower bound on
rotation-scaling-translation parameters estimation error. This bound depends on
texture roughness, noise level in reference and template images, correlation
between these images and geometrical transformation parameters.
