Morphology-based symbolic image modeling, multi-scale nonlinear smoothing, and pattern spectrum

The author develops a symbolic modeling of images based on their shape-size information. First, multiscale multishape structural distributions in the image are modeled by morphological openings, and a related shape-size descriptor, the pattern spectrum, is developed that can detect critical scales. Then the image is modeled as a nonlinear superposition of simpler parts (the symbols), which are translated and scaled shape patterns drawn from a finite collection. The model parameters are found by using the information from openings and pattern spectrum, and by local searches at points of generalized skeletons. The results appear promising for multiscale image analysis and shape recognition