Abstract

Recently, we proposed the generalized active contour model (g-snake) to model and extract deformable contours from noisy images. This paper demonstrates the usefulness of g-snake in classifying among several candidate deformable contours. The g-snake is suitable for this task because its shape representation is unique, affine invariant and possesses metric properties. We derive the optimal classification test and show that this requires marginalization of the distribution. However, as the summation is peaked around the posterior estimate in most practical applications, only small regions need to be considered. Finally, we performed extensive experimentations and report significant improvement over matched template in handwritten numeral recognition. 1 Introduction Recently, we proposed the generalized active contour model (g-snake) to simultaneously model and extract deformable contours from noisy images [1]. Unlike other deformable templates which consider only global [2, 3, 4] or l...

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