Otolith shape feature extraction oriented to automatic classification with open distributed data

The present study reviewed some of the critical pre-processing steps required for otolith shape characterisation for automatic classification with heterogeneous distributed data. A common procedure for optimising automatic classification is to apply data pre-processing in order to reduce the dimension of vector inputs. One of the key aspects of these pre-processing methods is the type of codification method used for describing the otolith contour. Two types of codification methods ( Cartesian and Polar) were evaluated, and the limitations ( loss of information) and the benefits ( invariance to affine transformations) associated with each method were pointed out. The comparative study was developed using four types of shape descriptors ( morphological, statistical, spectral and multiscale), and focused on data codification techniques and their effects on extracting shape features for automatic classification. A new method derived from the Karhunen - Loeve transformation was proposed as the main procedure for standardising the codification of the otolith contours.