IEEE Trans Image Process - Revisiting complex moments for 2-D shape representation and image normalization.

Tópicos

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Resumo

When comparing 2-D shapes, a key issue is their normalization. Translation and scale are easily taken care of by removing the mean and normalizing the energy. However, defining and computing the orientation of a 2-D shape is not so simple. In fact, although for elongated shapes the principal axis can be used to define one of two possible orientations, there is no such tool for general shapes. As we show in the paper, previous approaches fail to compute the orientation of even noiseless observations of simple shapes. We address this problem and show how to uniquely define the orientation of an arbitrary 2-D shape, in terms of what we call its Principal Moments. We start by showing that a small subset of these moments suffices to describe the underlying 2-D shape, i.e., that they form a compact representation, which is particularly relevant when dealing with large databases. Then, we propose a new method to efficiently compute the shape orientation: Principal Moment Analysis. Finally, we discuss how this method can further be applied to normalize gray-level images. Besides the theoretical proof of correctness, we describe experiments demonstrating robustness to noise and illustrating with real images.

Resumo Limpo

compar d shape key issu normal translat scale easili taken care remov mean normal energi howev defin comput orient d shape simpl fact although elong shape princip axi can use defin one two possibl orient tool general shape show paper previous approach fail comput orient even noiseless observ simpl shape address problem show uniqu defin orient arbitrari d shape term call princip moment start show small subset moment suffic describ under d shape ie form compact represent particular relev deal larg databas propos new method effici comput shape orient princip moment analysi final discuss method can appli normal graylevel imag besid theoret proof correct describ experi demonstr robust nois illustr real imag

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