1. A method for matching point regions in images with radial distortion that, given two frames acquired by a camera with radial distortion, it estimates a global image distortion and local transformations undergone by the image regions between frames, the method comprising: i. receiving a plurality of frames acquired from the camera with radial distortion; ii. selecting, using a processor, a set of image regions N i in a current frame T of the plurality of frames, with i=1, 2,. .. , K and K≧1; iii. selecting at least one motion model w with a vector of parameters p that describes one or more local deformations undergone by a plurality of generic image regions N in the case of the camera being a perspective camera with no lens distortion; iv. performing a 2-dimensional mapping function f, using the processor, for modelling the camera radial distortion such that a relation between a plurality of distorted image points x and a plurality of undistorted image points u, both expressed in a coordinate system with origin in the image center O, is given by u=f(x; ξ) with f defined as: 
 f ( x; ξ )=(1+ξ x T x ) −1 x, where ξ represents an image distortion parameter that quantifies the effect of a lens radial distortion in pixel units; v. deriving a warping model v c that combines the motion model w for perspective images and the radial distortion model f to describe the local deformations that arc undergone by the set of image regions N i , wherein the warping model v c is obtained by the formula: 
 v c ( x; q )= f −1 ( u; ξ)∘ w ( u; p ) ∘ g c ( x; ξ ) where q=(p ξ) is the vector of the warp parameters, f 1 denotes the inverse of the 2-dimensional mapping function f, w is the motion model, and g c is an approximation of the mapping function f with formula: 
 g c ( x, ξ)=(1+ξ c T c ) −1 x with c being the coordinates of a center of a considered image region of the set of image regions; vi. deriving a cost function that provides a cumulative alignment error ε in aligning the K image regions in the current frame T, with the putative corresponding regions in a following frame I, and that has the form: ε = ∑ i = 1 K ∑ x ∈  i [ I ( v c ( x ; q i ) ) - T ( x ) ] 2 where q i is 
 q i =( p, ξ), with p i being a vector of parameters of the motion model w for the case of a particular image region N i and ξ being the global image distortion parameter; vii. estimating jointly, using the processor, the local motion parameters p i of each of the image regions and the global image distortion parameter ξ using an iterative approach for minimizing the cumulative alignment error ε; viii. updating, using the processor, a position and appearance of the set of image regions N i using the transformation v c derived in step (v) with the parameters p i and ξ estimated in step (vii).