Abstract: The present invention relates to a method and system for segmenting texture of multi-dimensional data indicative of a characteristic of an object. Received multi-dimensional data are transformed into second multi-dimensional data within a Stockwell domain based upon a polar S-transform of the multi-dimensional data. Principal component analysis is then applied to the second multi-dimensional data for generating texture data characterizing texture around each data point of the multi-dimensional data. Using a classification process the data points of the multi-dimensional data are partitioned into clusters based on the texture data. Finally, a texture map is produced based on the partitioned data points. The present invention provides image texture segmentation based on the polar S-transform having substantially reduced redundancy while keeping maximal data variation.

Abstract: The present invention relates to a method and system for processing time-varying three-component signals to determine polarization dependent features therefrom. Relation data indicative of a predetermined expression relating ellipse elements describing an ellipse in three dimensional space to the components of the three-component signal are provided. Received signal data indicative of a three-component signal are processed for transforming each component of the three-component signal into time-frequency domain providing second signal data. Data indicative of polarization dependent features are then determined using the relation data and the second signal data. The method and system for processing time-varying three-component signals according to the invention are highly beneficial in numerous applications for evaluating three-component signal data such as seismology.

Abstract: The present invention relates to a method and system for processing time series signal data or image signal data indicative of a characteristic of an object. Received signal data are transformed into second signal data within a Stockwell domain based upon a sparse approximation of a S-transform of the signal data. The second signal data are then processed within the Stockwell domain to extract features therefrom. The processing includes determination of local spectra at predetermined locations; determination of voices at predetermined frequencies; and filtering of the signal data using a filter function in dependence upon frequency and time or space. The signal processing method and system according to the invention enables signal processing based on the S-transform using a desktop computer or workstation.

Abstract: The present invention relates to a method and system for segmenting texture of multi-dimensional data indicative of a characteristic of an object. Received multi-dimensional data are transformed into second multi-dimensional data within a Stockwell domain based upon a polar S-transform of the multi-dimensional data. Principal component analysis is then applied to the second multi-dimensional data for generating texture data characterizing texture around each data point of the multi-dimensional data. Using a classification process the data points of the multi-dimensional data are partitioned into clusters based on the texture data. Finally, a texture map is produced based on the partitioned data points. The present invention provides image texture segmentation based on the polar S-transform having substantially reduced redundancy while keeping maximal data variation.