Abstract: One embodiment of the invention provides an image processing method for use in fitting a deformable shape model to an acquired image. The deformable shape model specifies a set of target points whose motion is governed by the model. The method comprises, for each target point, generating a corresponding response image by: providing a feature detector to locate a given target point within the acquired image, wherein said feature detector includes a random forest comprising one or more decision trees; scanning a patch image across the acquired image to define multiple sampling locations with respect to the acquired image; for each sampling location of the patch image with respect to the acquired image, performing regression voting using the random forest to produce one or more votes for the location of the given target point within the acquired image; and accumulating the regression votes for all of the multiple sampling locations to generate said response image corresponding to the given target point.

Abstract: One embodiment of the invention provides an image processing method for use in fitting a deformable shape model to an acquired image. The deformable shape model specifies a set of target points whose motion is governed by the model. The method comprises, for each target point, generating a corresponding response image by: providing a feature detector to locate a given target point within the acquired image, wherein said feature detector includes a random forest comprising one or more decision trees; scanning a patch image across the acquired image to define multiple sampling locations with respect to the acquired image; for each sampling location of the patch image with respect to the acquired image, performing regression voting using the random forest to produce one or more votes for the location of the given target point within the acquired image; and accumulating the regression votes for all of the multiple sampling locations to generate said response image corresponding to the given target point.

Abstract: A method of building a statistical shape model by automatically establishing correspondence between a set of two dimensional shapes or three dimensional shapes, the method comprising: a) determining a parameterisation of each shape, building a statistical shape model using the parameterisation, using an objective function to provide an output which indicates the quality of the statistical shape model; performing step a) repeatedly for different parameterisations and comparing the quality of the resulting statistical shape models using output of the objective function to determine which parameterisation provides the statistical shape model having the best quality, wherein the output of the objective function is a measure of the quantity of information required to code the set of shapes using the statistical shape model.