Abstract: Method for organizing a set of images into subsets of images, the method including the following procedures producing a respective model according to a plurality of feature points and the geometric relations between the feature points for each of the images, determining a similarity index between each pair of the images, according to the respective model of each image of the pair of the images, producing a distance matrix, according to the similarity index between each pair of the images, producing a set of coordinates, according to the distance matrix and sorting the images in plurality of dimensions, according to the set of coordinates.

Abstract: Method for organizing a set of images into subsets of images, the method including the following procedures producing a respective model according to a plurality of feature points and the geometric relations between the feature points for each of the images, determining a similarity index between each pair of the images, according to the respective model of each image of the pair of the images, producing a distance matrix, according to the similarity index between each pair of the images, producing a set of coordinates, according to the distance matrix and sorting the images in plurality of dimensions, according to the set of coordinates.

Abstract: A method for fast motion estimation determines the relative motion between a first and a second image by using either a bi-directional gradient method (BDGM) or a symmetric gradient method (SGM) approach. The global motion, defined by a plurality of parameters in which each parameter has an interval, is estimated by providing an initial estimate of two translation parameters each having an interval of values, dividing each interval into two non-equal (BDGM) or equal (SGM) sub-intervals, and using an iterative process starting with the initial estimate of the two equal sub-intervals. The iterative process calculates the optimal value (BDGM) or a center value (SGM) of the value interval of each parameter and yields a final parameters vector defining the global motion. The bi-directional and the symmetric gradient methods provide faster convergence and smaller linearization error, or convergence in cases where regular gradient methods do not converge.