Nonstationary autoregressive modeling of object contours

A spatially variant circular autoregressive (SVCAR) model is introduced for the analysis and classification of closed shape boundaries. The model represents a closed shape boundary sequence as the output of a nonstationary all-pole linear system (driven by white noise) whose coefficient's spatial evolution can be expressed as a truncated function expansion. Features derived from the SVCAR model are shown to be invariant to shape scaling, rotation, and translation. A shape-matching algorithm is developed to optimally adjust the SVCAR model coefficients for changes in contour sequence starting point. Laboratory experiments involving object sets representative of industrial, military, and geographic shapes are presented. Superior classification results are demonstrated.