Method and arrangement for image model construction

摘要

A method for constructing an image model (M1; M) from at least one image data input (IV1; IV1-IVn), comprises the steps of, in an iterative way,
determining at least one state (PS1; PS1-PSn) of said at least one image data input (IV1; IV1-IVn), and a state (PSMF) of an intermediate learning model (MF; MIF)determining a target state (TSP) from said at least one state (PS1; PS1-PSn) of said at least one image data input, and from the state (PSMF) of said intermediate learning model (MF; MIF),performing at least one transformation in accordance with the determined target state (TSP) on said at least one image data input (IV1; IV1-IVn), thereby generating at least one transformed image (IV1T; IV1T-IVnT),aggregating said at least one transformed image (IV1T; IV1T-IVnt) with intermediate learning model (MF; MIF; MIT; MFT) information, thereby generating an updated estimate of said image model (M1; M),providing said updated estimate of said image model (M1; M) as said image model (M1; M) while alsoproviding said updated estimate of said image model (M1; M) in a feedback loop to a model object learning module (500) for deriving an update of said intermediate learning model (MF, MIF).

主权项

1. A method for constructing an image model of an object from at least one image data input, the method comprising the steps of:
extracting at least one state comprising a configuration of object features, the features being represented by a set of values of the at least one image data input, and a state comprising a configuration of object features, the features being represented by a set of values of an intermediate learning model; determining a target state by performing a weighted combination of the at least one state of the at least one image data input, and of the state of the intermediate learning model, with the weights reflecting confidences of the states, the confidences being determined during the state extraction; performing at least one image transformation by using the determined target state on the at least one image data input, thereby generating at least one transformed image; aggregating the at least one transformed image with intermediate learning model information into a single two-dimensional or three-dimensional image, thereby generating an updated estimate of the image model; providing the updated estimate of the image model as the image model to an output while also providing the updated estimate of the image model in a feedback loop to a model object learning module for deriving an update of the intermediate learning model; and repeating the previous steps in an iterative process.