Discrimination of Natural Contours by Means of Time-Scale-Frequency Decompositions

This paper evaluates the discriminative potential of time-scale-frequency decompositions for contour-based recognition of natural shapes. Specifically, it provides the analysis and comparison of descriptors derived from the Fourier Transform, the Short-Time Fourier Transform, the Wavelet Transform and the Multi-Resolution Fourier Transform. Linear Discriminant Analysis and Backward Sequential Selection are employed for dimensionality reduction and selection of the most significant features, respectively. A Bayesian Classifier is used for class discrimination. To improve discrimination, a hierarchical classification is adopted. The approaches are analyzed and compared considering experiments developed over digitalized leaves.