Contour models for curvature estimation and shape decomposition

A statistical contour model is developed. A digital contour is modeled by a noisy observation which is represented by polynomial functions of coordinate variables. To estimate curvature functions of digital contours, the authors develop maximum likelihood estimators by fitting the model over a small neighborhood. The neighborhood size is determined by a maximum likelihood decision rule. Statistical properties of the estimators are also investigated. The contour is decomposed at curvature extrema points by finding zero-crossings of the first derivative of estimated curvature function. Experimental results show that the model based approach performs better in estimating curvature functions and detecting extrema points than other conventional approaches based on low-pass filtered curvature functions.<>