The invention concerns a method to compare two data obtained from a sensor or interface, carried out by processing means of a processing unit, the method comprising the computing of a similarity function between two feature vectors of the data to be compared, characterized in that each feature vector of a datum is modelled as the summation of Gaussian variables, said variables comprising: a mean of a class to which the vector belongs, an intrinsic deviation, and an observation noise of the vector, each feature vector being associated with a quality vector comprising information on the observation noise of the feature vector, and in that the similarity function is computed from the feature vectors and associated quality vectors.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method to compare two data obtained from a sensor or interface, implemented by a processor, the method comprising computing a similarity function between two feature vectors x,y of the data to be compared, Wherein each feature vector of a datum is modelled as the summation of three independent Gaussian variables μ+ω+ε, where said variables are: a mean μ of a class to which the vector belongs, an intrinsic deviation ω, and an observation noise ε of the vector, each feature vector x being associated with a quality vector qx comprising information on the observation noise of the feature vector, the components of the quality vector being generated as a function of type of datum and type of features forming the feature vector, and wherein the similarity function is computed from the feature vectors x,y and the associated quality vectors as a function of the covariance matrixes of the components of the feature vectors, and the observation noise covariance matrix of each feature vector is obtained as a function of the associated quality vector. 2. The comparison method according to claim 1, wherein the covariance matrixes of the components of the feature vectors comprise the covariance matrices that are respectively the covariance matrix of the means of the classes to which the vectors belong called inter-class covariance matrix, and the covariance matrix of vector deviations from the class means called intra-class covariance matrix. 3. The comparison method according to claim 1, wherein the similarity function is the logarithm of the ratio between the probability density P(x,y|Hsim,Sεx,Sεy) of the feature vectors with the vectors belonging to one same class, and the probability density P(x,y|Hdis,Sεx,Sεy) of the feature vectors with the vectors belonging to two different classes. 4. The comparison method according to claim 1, further comprising the application of a learning algorithm to determine the covariance matrices of the means of the classes to which the vectors belong and of the deviations of the vectors from the class means. 5. The comparison method according to claim 4, wherein the learning algorithm is an algorithm of expectation-maximization type. 6. The comparison method according to claim 1, wherein the similarity function is given by the formula: LR(x,y|Sεx,Sεy) =xT(A−(Sμ+Sω+Sεx)−1)x+yT(C−(Sμ+Sω+Sεy)−1)y+2xTBy −log|Sμ+Sω+Sεx|−log|A|+constant where: A=(Sμ+Sω+Sεx−Sμ(Sμ+Sω+Sεy)−1Sμ)−1 B=−ASμ(Sμ+Sω+Sεy)−1 C=(Sμ+Sω+Sεy)−1 (I+SμASμ(Sμ+Sω+Sεy)−1) and where Sμ is the covariance matrix of the means of the classes, Sω is the covariance matrix of the deviations from a mean, and Sεx and Sεy are the covariance matrices of the observation noises of vectors x and y respectively. 7. The comparison method according to claim 1, wherein the computer data derived from sensors or interfaces are data representing physical objects or physical magnitudes. 8. The comparison method according to claim 7, wherein the computer data derived from sensors or interfaces are images, and the feature vectors are obtained by applying at least one filter to the images. 9. The comparison method according to claim 1, further comprising the comparison of the result of the computed similarity function with a threshold to determine whether the data belong to a common class. 10. A computer programme product comprising code instructions to carry out the method according to claim 1 when it is executed by a processor. 11. A system comprising: a database comprising a plurality of so-called labelled data; a data acquisition unit; and a processing unit comprising a processor adapted to construct two feature vectors and two associated quality vectors from two data items, said processing unit also being adapted to compare the data by implementing the method according to claim 1.