k-dimensional Size Functions for Shape Description and Comparison

This paper advises the use of k-dimensional size functions for comparison and retrieval in the context of multidimensional shapes, where by shape we mean something in two or higher dimensions having a visual appearance. The attractive feature of k-dimensional size functions is that they allow to readily establish a similarity measure between shapes of arbitrary dimension, taking into account different properties expressed by a multivalued real function defined on the shape. This task is achieved through a particular projection of k-dimensional size functions into the 1-dimensional case. Therefore, previous results on the stability for matching purposes become applicable to a wider range of data. We outline the potential of our approach in a series of experiments.

• Type: Conference
• Year: 2007
• Where: ICIAP
• Authors: Andrea Cerri, Silvia Biasotti, Daniela Giorgi
• Keywords: ICIAP 2007, Image Processing, K-dimensional Size Functions, Multidimensional Shapes, Similarity Measure