A computer-implemented method for assessing quality of an image of an object, the method comprising: determining a similarity of features of two or more sub-images of the image to a model of the object which is based on multiple training images of multiple different objects of a same type; the model being comprised of sub-models, wherein generation of each sub-model includes extracting features from multiple sub-images at a corresponding sub-image location of the multiple training images, wherein a multi-dimensional feature vector is extracted for each sub-image of each training image; and performing a respective statistical modelling process on said extracted features to create the sub-model, wherein each sub-model is characterised by a mean vector and a covariance matrix which are based on computed multi-dimensional feature vectors of a sub-image of each training image that the sub-model is based on; wherein each sub-model corresponds to a sub-image of the image, the sub-image of the image being aligned with the sub-images of each training images the corresponding sub-model is based on, wherein determining similarity is based on the similarity of features of each sub-image to features modelled by the corresponding sub-model, wherein the sub-models are modelled individually, each sub-model being modelled independently of image data used to determine other sub-models; and determining the quality of the image based on the determined similarity of the two or more sub-images. determining a similarity of features of two or more sub-images of the image to a model of the object which is based on multiple training images of multiple different objects of a same type; the model being comprised of sub-models, wherein generation of each sub-model includes extracting features from multiple sub-images at a corresponding sub-image location of the multiple training images, wherein a multi-dimensional feature vector is extracted for each sub-image of each training image; and performing a respective statistical modelling process on said extracted features to create the sub-model, wherein each sub-model is characterised by a mean vector and a covariance matrix which are based on computed multi-dimensional feature vectors of a sub-image of each training image that the sub-model is based on; extracting features from multiple sub-images at a corresponding sub-image location of the multiple training images, wherein a multi-dimensional feature vector is extracted for each sub-image of each training image; and performing a respective statistical modelling process on said extracted features to create the sub-model, wherein each sub-model is characterised by a mean vector and a covariance matrix which are based on computed multi-dimensional feature vectors of a sub-image of each training image that the sub-model is based on; wherein each sub-model corresponds to a sub-image of the image, the sub-image of the image being aligned with the sub-images of each training images the corresponding sub-model is based on, wherein determining similarity is based on the similarity of features of each sub-image to features modelled by the corresponding sub-model, wherein the sub-models are modelled individually, each sub-model being modelled independently of image data used to determine other sub-models; and determining the quality of the image based on the determined similarity of the two or more sub-images.