VISUALIZATION OF MULTIDIMENSIONAL SHAPE AND TEXTURE FEATURES IN LASER RANGE DATA USING COMPLEX-VALUED GABOR WAVELETS

This paper describes a new method for visualization and analysis of multivariate laser range data using complex-valued non-orthogonal Gabor wavelets, principal component analysis and a topological mapping network. The initial data set that provides both shape and texture information is encoded in terms of both amplitude and phase of a complex valued 2D image function. A set of carefully designed oriented Gabor filters performs a decomposition of the data and allows for retrieving local shape and texture features. The feature vector obtained from this method is multidimensional and in order to evaluate similar data features, further subspace methods to transform the data onto visualizable attributes, such as R, G, B, have to be determined. For this purpose, a feature-based visualization pipeline is proposed consisting of principal component analysis, normalization and a topological mapping network. This process finally renders a R, G, B subspace representation of the multidimensional feature vector. Our method is primarily applied to the visual analysis of features in human faces-but is not restricted to that.