2063 results about "Similarity measure" patented technology

Systems and methods enabling search of a repository for the location of data that is similar to input data, using a defined measure of similarity, in a time that is independent of the size of the repository and linear in a size of the input data, and a space that is proportional to a small fraction of the size of the repository. The similar data segments thus located are further analyzed to determine their common (identical) data sections, regardless of the order and position of the common data sections in the repository and input, and in a time that is linear in the segment size and in constant space.

A method and system is provided for registering a 2D radiographic image of a target with previously generated 3D scan data of the target. A reconstructed 2D image is generated from the 3D scan data. The radiographic 2D image is registered with the reconstructed 2D images to determine the values of in-plane transformation parameters (x, y, θ) and out-of-plane rotational parameters (r, Φ), where the parameters represent the difference in the position of the target in the radiographic image, as compared to the 2D reconstructed image. An initial estimate for the in-plane transformation parameters is made by a 3D multi-level matching process, using the sum-of-square differences similarity measure. Based on these estimated parameters, an initial 1-D search is performed for the out-of-plane rotation parameters (r, Φ), using a pattern intensity similarity measure. The in-plane parameters (x, y, θ) and out-of-plane parameters (r, Φ) are iteratively refined, until a desired accuracy is reached.

A system and method for estimating parameters for features of a translation scoring function for scoring candidate translations in a target domain are provided. Given a source language corpus for a target domain, a similarity measure is computed between the source corpus and a target domain multi-model, which may be a phrase table derived from phrase tables of comparative domains, weighted as a function of similarity with the source corpus. The parameters of the log-linear function for these comparative domains are known. A mapping function is learned between similarity measure and parameters of the scoring function for the comparative domains. Given the mapping function and the target corpus similarity measure, the parameters of the translation scoring function for the target domain are estimated. For parameters where a mapping function with a threshold correlation is not found, another method for obtaining the target domain parameter can be used.

A method of registering 3-dimensional digitized images to 2-dimensional digitized images during a medical procedure includes providing a pair of correctly-registered training images L={lr, lf} and their joint intensity distribution pl(ir, if), wherein ir and if are reference and floating images, respectively, providing a pair of observed images O={or, of} and their joint intensity distribution po(ir, if), mapping a marginal intensity distribution of the observed pair O={or, of} to a marginal intensity distribution of the training pair L={lr, lf}, and estimating a set of parameters T that registers image of to image or by maximizing a weighted sum of a Jensen-Shannon divergence (JSD) of a joint intensity distribution of the observed pair and a joint intensity distribution of the training pair and a similarity measure between the observed images.

The similarity between two data objects of the same type (e.g., two resumes, two job descriptions, etc.) is determined using predictive modeling. A basic assumption is that training datasets are available containing compatibility measures between objects of the first type and data objects of a second type, but that training datasets measuring similarity between objects of the first type are not. A first predictive model is trained to assess compatibility between data objects of a first type and data objects of a second type. Then, in one scenario, pairs of objects of the first type are compared for similarity by running them through the first predictive model as if one object of the pair is an object of the first type and the other object of the pair is an object of the second type. Alternatively, for each object in a set of objects of the first type, the first predictive model is used to create a respective vector of compatibility scores against a fixed set of objects of the second type; these various vectors are then used to derive measures of similarity between pairs of objects of the first type, from which a second predictive model is trained, and the second predictive model is then used to assess the similarity of pairs of objects of the first type.