Symbolic signal representation using nonlinear filtering

A symbolic modeling method is developed for images and imagelike signals based on their shape-size structure. A signal is represented as a minimal nonlinear superposition of simpler parts, namely, the symbols, which are translated and scaled shape patterns drawn from a finite collection. To obtain the model parameters, multiscale morphological openings and the related pattern spectrum are used to help decide which shape patterns exist in the signal and at which scales. Then the pattern locations are found via a local search at points of generalized morphological skeletons. This model is useful for signal abstractions and object recognition