In this paper we compare the quality of three different principle component analysis (PCA) based methods to generate transfer functions for the 3D visualization of imaging spectroscopy data. We discuss three criteria for judging the quality of features in these visualizations. These criteria are used to interpret visualizations of features in the brain of the snail Lymnaea Stagnalis. We show that the PCA method that uses model additional information, clearly results in superior visualizations.

Additional Metadata
Publisher Acta Press
Editor J.J. Villanueva
Citation
Broersen, A, van Liere, R, & Heeren, R.M.A. (2005). Comparing three PCA-based methods for the 3D visualization of imaging spectroscopy data. In J.J Villanueva (Ed.), Proceedings of the fifth International Conference Visualization, imaging, and image processing (VIIP 2005), Sept. 7-9, 2005, Benidorm, Spain (pp. 540–545). Acta Press.