@inbook{3271, abstract = {In this paper we present an efficient framework for computation of persis- tent homology of cubical data in arbitrary dimensions. An existing algorithm using simplicial complexes is adapted to the setting of cubical complexes. The proposed approach enables efficient application of persistent homology in domains where the data is naturally given in a cubical form. By avoiding triangulation of the data, we significantly reduce the size of the complex. We also present a data-structure de- signed to compactly store and quickly manipulate cubical complexes. By means of numerical experiments, we show high speed and memory efficiency of our ap- proach. We compare our framework to other available implementations, showing its superiority. Finally, we report performance on selected 3D and 4D data-sets.}, author = {Wagner, Hubert and Chen, Chao and Vuçini, Erald}, booktitle = {Topological Methods in Data Analysis and Visualization II}, editor = {Peikert, Ronald and Hauser, Helwig and Carr, Hamish and Fuchs, Raphael}, pages = {91 -- 106}, publisher = {Springer}, title = {{Efficient computation of persistent homology for cubical data}}, doi = {10.1007/978-3-642-23175-9_7}, year = {2011}, }