This invention relates to a global
feature tracking method for time-varying data based on
unsupervised segmentation. It constructs a volume data segmentation network to segment the input volume data and achieve global tracking. This invention introduces
deep learning to segment volume data, separating feature domains from the background. This enables automatic and accurate tracking of specific features in complex data without any
manual annotation, reducing tracking complexity and improving tracking accuracy. It introduces a global
feature tracking method; by selecting a target feature, it can track the trajectories of features similar to the target feature across all time steps. Users can select any feature at any
time step to track features of interest in time-varying data. It can track extracted features from a global perspective, avoiding tracking errors and defects caused by local tracking methods. Furthermore, it adds the tracking of spatiotemporally similar features, simultaneously tracking the complete paths of spatially similar features of the target feature, improving
feature tracking accuracy.