Morphometrical data analysis using wavelets

In this paper, we present a new shape analysis approach using the well-known wavelet transform and exploring shape representation by landmarks. First, we describe the approach adopted to represent the landmarks data as parametric signals. Then, we show the relation of the derivatives of Gaussian wavelet transform applied to the signal-to-differential properties of the shape that it represents. We present experimental results using real data to show how it is possible to characterize shapes through multiscale and differential signal-processing techniques in order to relate morphological variables with phylogenetic signal, environmental factors and sexual dimorphism. The goal of this research is to develop an effective wavelet transform-based method to represent and classify multiple classes of shapes given by landmarks.