Maximum likelihood decision for recognition of noisy shapes
Kie Bum Eom, X. Chen · 2003
An algorithm is developed to recognize shapes by a maximum-likelihood decision method after representing the contour of a shape by an autoregressive model. A decision rule is developed to test the similarity of objects pairwise. The rule is given in terms of the parameter estimates. The recognition of an arbitrary number of objects is accomplished by applying the decision rules to all possible pairwise combinations. The contour recognition algorithm developed is applied to contours of seven different machine parts and five different aircraft shapes. Without additive noise, all seven machine parts are classified 100% correctly. Contours of images contaminated by additive white Gaussian noise are tested. The proposed method has performed better than conventional methods on noisy images.>