Abstract: A method of locating features of an object, of a class of objects, of a class of objects, within a target image. The method comprises initializing a set of feature points within the target image, each feature point corresponding to a predetermined feature for objects of the class of objects; deriving a set of template detectors, from the set of feature points, using a statistical model of the class of objects, each template detector comprising an area of image located about the location of a feature point for an object of the class of objects; comparing the set of template detectors with the target image; and updating the set of feature points within the target image in response to the result of the comparison.

Abstract: A method of locating features of an object, of a class of objects, of a class of objects, within a target image. The method comprises initialising a set of feature points within the target image, each feature point corresponding to a predetermined feature for objects of the class of objects; deriving a set of template detectors, from the set of feature points, using a statistical model of the class of objects, each template detector comprising an area of image located about the location of a feature point for an object of the class of objects; comparing the set of template detectors with the target image; and updating the set of feature points within the target image in response to the result of the comparison.

Abstract: A method of identifying an object class uses a model based upon appearance parameters derived by comparing images of objects of different classes. The model includes a representation of a probability density function describing a range over which appearance parameters may vary for a given class of object, the model further including a defined relationship between the appearance parameters and the probability density function. The method generates appearance parameters representative of an unknown object, estimates an appropriate probability density function for the unknown object using the defined relationship between the appearance parameters and the probability density function, then iteratively modifies at least some of the appearance parameters within limits determined using the probability density function to identify the object class.