Video-rate eigenspace methods for position tracking and remote monitoring

Schuurman, Derek C.; Capson, David W.

doi:10.1109/iai.2002.999887

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…It is a well-recognized property of moments that they can be used to reconstruct the original function, that is, none of the original image information is lost in the projection of the image onto the moment basis functions, assuming an infinite number of moments are calculated. This is also consistent with work on other types of reconstruction, such as eigenanalysis where it has been found that increasing numbers of eigenvectors are required to capture image detail [28] and again exceed the number required for recognition. Describing images with moments instead of other more commonly used image features means that global properties of the image are used rather than local properties.…”

Section: A Moment-based Image Reconstruction Methodssupporting

confidence: 83%

Removal of blur in images based on least squares solutions

Stanimirović

Chountasis

Pappas

et al. 2013

Math Methods in App Sciences

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We propose an image restoration method. The method generalizes image restoration algorithms that are based on the Moore–Penrose solution of certain matrix equations that define the linear motion blur. Our approach is based on the usage of least squares solutions of these matrix equations, wherein an arbitrary matrix of appropriate dimensions is included besides the Moore–Penrose inverse. In addition, the method is a useful tool for improving results obtained by other image restoration methods. Toward that direction, we investigate the case where the arbitrary matrix is replaced by the matrix obtained by the Haar basis reconstructed image. The method has been tested by reconstructing an image after the removal of blur caused by the uniform linear motion and filtering the noise that is corrupted with the image pixels. The quality of the restoration is observable by a human eye. Benefits of using the method are illustrated by the values of the improvement in signal‐to‐noise ratio and in the values of peak signal‐to‐noise ratio. Copyright © 2013 John Wiley & Sons, Ltd.

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Section: A Moment-based Image Reconstruction Methodssupporting

confidence: 83%

Removal of blur in images based on least squares solutions

Stanimirović

Chountasis

Pappas

et al. 2013

Math Methods in App Sciences

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show abstract

“…This is also consistent with work on other types of reconstruction, such as eigenanalysis where it has been found that increasing numbers of eigenvectors are required to capture image detail [7] and again exceed the number required for recognition. Thus, when considering image reconstruction from moments, the number of moments required for accurate reconstruction will be related to the frequencies present within the original image.…”

Section: Reconstruction From Zernike Momentssupporting

confidence: 75%

On Moving Object Reconstruction By Moments

Prismall

Nixon

Carter

2002

Procedings of the British Machine Vision Conference 2002

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Recent research using statistical moments to describe moving shapes through an image sequence has led to an interest in reconstructing moving shapes from their moment description. This paper discusses how the moment description through a series of frames might be used to predict missing or intermediate frames within a sequence. Additionally, this highlights generic aspects of moment reconstruction which rarely receive more than scant attention. The ideas presented use Zernike moments, although the general framework is applicable to all types of moments. We show how a moving human silhouette can be reconstructed with accuracy by interpolation from a moment history.

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