Patent application number | Description | Published |
20100135574 | Image processing using neural network - Image processing method wherein each image is composed of an array of image points, so called pixels or voxels particularly in a two-, three-, or more dimensional space respectively each image point being univocally defined by its position within the array of image points and by one or more numerical parameters defining the image point appearance as regards characteristics of brightness, grey, colour shade or the like, and wherein each image point is considered to be a node of an artificial neural network, the image being processed as a function of parameters defining the appearance of each pixel as values of the nodes of said artificial neural network and as a function of connections of each pixel under processing with neighbouring pixels composed of pixels of a predetermined subset of pixels, particularly with neighbouring pixels of said pixel under processing, so called pixel window, while pixels of the new image i.e. of the processed image are obtained by iterative evolution steps of parameters defining the appearance such as evolution steps of the value of nodes or by iterative evolution steps of values of the set of connections or by a combination of said evolutions, wherein the processing occurs by evolution iterative steps where each step is a function also of connections of neighbouring pixels with the pixel under examination, when each of said neighbouring pixels of the pixel under examination is considered also as a neighbouring pixel of one ore more or all pixels adjacent to said neighbouring pixel, which function is an immediate feedback contribution for determining appearance values of all other pixels. | 06-03-2010 |
20100217145 | Method of processing multichannel and multivariate signals and method of classifying sources of multichannel and multivariate signals operating according to such processing method - A method of processing multichannel and multivariate signals as described hereinbefore, wherein the signals from each channel are subjected to a first processing step by a recirculation artificial neural network being trained to generate the recorded multichannel and multivariate signals; and a second processing step in which the weights of the connections between the knots of the recirculation neural network determined in the first processing step are processed by an artificial neural network, the recirculation neural network being preferably of the non supervised kind. A particular family of recirculation neural network which can be used according to the present invention is a so called auto-associative neural network. The method further provides, in combination, the use of a predictive and/or classification and/or clustering algorithm for determining the qualities or features of objects from the multichannel multivariate signals generated by said object, the weight matrix obtained by processing said multichannel and multivariate signals with a self-associated neural network being used as records for representing said multichannel and multivariate signals. The method is used for patients suffering from neurological disorders for analysing and evaluating the EEG patterns of these patients. | 08-26-2010 |
20120154398 | Method of determining implicit hidden features of phenomena which can be represented by a point distribution in a space - A method of determining implicit hidden features of phenomena, representable by a point distribution in a space, includes the following steps:
| 06-21-2012 |
20120155715 | Method of determining features of events or processes having a dynamic evolution in space and/or time - A method of determining features of events or processes having a dynamic evolution in space and/or time using measurements of parameters that calculate the most probable consequences of the event or process at a certain time includes:
| 06-21-2012 |
20120158373 | Model simulating the evolutionary dynamics of events or processes and method of generating a model simulating the evolutionary dynamics of events or processes - A model simulating the evolutionary dynamics of events or processes includes a non linear adaptive mathematical system simulating the spatial and temporal dynamics of the event or processes by using measured values of a certain number of parameters describing the evolutionary condition of the event or process at certain different times. The values of such parameters are measured at a first time and at least a second time different from and following the first time. The model enables the definition of a n-dimensional array of points in a n-dimensional reference system having an axis that represents the values of the parameters being measured, the parameters in the array being represented by special points in the array of points. The displacements of each of the points of the array of points are computed as a function of the displacements in the array of points of each of the points representing the measured parameter values between a first time of measurement and at least a successive second time of measurement and as a function of the distance of each of the points of the array of points from each of the points representing the measured parameters. The evolution of the event and or the model in time is visualized by displaying the points of the array of points at different times. | 06-21-2012 |
20120246101 | Model for reconstructing a causation process from time varying data describing an event and for predicting the evolution dynamics of the event - A method of reconstructing a causation process from time varying data describing an event, the data consisting in a certain number of entities each having a position in a space, and each of the entities being characterized by at least a quantity or value relatively to at least one feature and in the quantity or value relatively to at least one of the features of the entities at least at two different times or at each time instant of a sequence of time instants;
| 09-27-2012 |
20120263371 | Method of image fusion - A method of fusing images includes the steps of providing at least two images of the same object, each image being a digital image or being transformed in a digital image formed by an array of pixels or voxels, and of combining together the pixels or voxels of the at least two images being combined to obtain a new image formed by the combined pixels or voxels. | 10-18-2012 |