Abstract: There is provided a method of image classification. The method includes: providing a set of category mapping discriminators, each corresponding to a respective category, wherein each category mapping discriminator of the set of category mapping discriminators is configured for discriminating features relating to input images that belong to the respective category of the category mapping discriminator; extracting a plurality of features from an input image using a machine learning model; determining, for each of the set of category mapping discriminators, an output value based on the plurality of extracted features using the category mapping discriminator; and determining a classification of the input image based on the output values of the set of category mapping discriminators.

Abstract: There is provided a method of image classification. The method includes: providing a set of category mapping discriminators, each corresponding to a respective category, wherein each category mapping discriminator of the set of category mapping discriminators is configured for discriminating features relating to input images that belong to the respective category of the category mapping discriminator; extracting a plurality of features from an input image using a machine learning model; determining, for each of the set of category mapping discriminators, an output value based on the plurality of extracted features using the category mapping discriminator; and determining a classification of the input image based on the output values of the set of category mapping discriminators.

Abstract: A three-dimensional model of a reconstructed bone framework is obtained using an iterative, surface interpolating algorithm. A reference and a target three-dimensional model are provided. The reference model is non-rigidly registered to the target model based upon positional constraints, so as to produce a registered reference model. An initial reconstructed model is set as the registered reference model. A first correspondence search is iteratively conducted to identify a first set of corresponding points on the reconstructed and target models. During each iteration, the reconstructed model is incrementally and non-rigidly registered to the target model based upon the corresponding points. A second correspondence search is conducted to identify a second set of corresponding points on the reconstructed and target models.

Abstract: A three-dimensional model of a reconstructed bone framework is obtained using an iterative, surface interpolating algorithm. A reference and a target three-dimensional model are provided. The reference model is non-rigidly registered to the target model based upon positional constraints, so as to produce a registered reference model. An initial reconstructed model is set as the registered reference model. A first correspondence search is iteratively conducted to identify a first set of corresponding points on the reconstructed and target models. During each iteration, the reconstructed model is incrementally and non-rigidly registered to the target model based upon the corresponding points. A second correspondence search is conducted to identify a second set of corresponding points on the reconstructed and target models.