Flow feature extraction based on entropy and Clifford algebra

Abstract

Feature extraction is important to the visualization of large scale flow fields. To extract flow field features, we propose a new method that is based on Clifford algebra and information entropy theory. Given an input 3D flow field defined on uniform grids, it is firstly converted to a multi-vector field. We then compute its flow entropy field according to information theory, and choose high entropy regions to do the Clifford convolution with predefined multi-vector filter masks. Features are determined on the convolution results. With this method, we can locate, identify, and visualize a set of flow features. And test results show that our method can reduce computation time and find more features than the topology-based method.