IEEE Trans Image Process - Combined invariants to similarity transformation and to blur using orthogonal Zernike moments.

Tópicos

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Resumo

The derivation of moment invariants has been extensively investigated in the past decades. In this paper, we construct a set of invariants derived from Zernike moments which is simultaneously invariant to similarity transformation and to convolution with circularly symmetric point spread function (PSF). Two main contributions are provided: the theoretical framework for deriving the Zernike moments of a blurred image and the way to construct the combined geometric-blur invariants. The performance of the proposed descriptors is evaluated with various PSFs and similarity transformations. The comparison of the proposed method with the existing ones is also provided in terms of pattern recognition accuracy, template matching and robustness to noise. Experimental results show that the proposed descriptors perform on the overall better.

Resumo Limpo

deriv moment invari extens investig past decad paper construct set invari deriv zernik moment simultan invari similar transform convolut circular symmetr point spread function psf two main contribut provid theoret framework deriv zernik moment blur imag way construct combin geometricblur invari perform propos descriptor evalu various psfs similar transform comparison propos method exist one also provid term pattern recognit accuraci templat match robust nois experiment result show propos descriptor perform overal better

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