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Proceedings Paper

Wavelet applied to computer vision in astrophysics
Author(s): Albert Bijaoui; Eric Slezak; Myriam Traina
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Paper Abstract

Multiscale analyses can be provided by application wavelet transforms. For image processing purposes, we applied algorithms which imply a quasi isotropic vision. For a uniform noisy image, a wavelet coefficient W has a probability density function (PDF) p(W) which depends on the noise statistic. The PDF was determined for many statistical noises: Gauss, Poission, Rayleigh, exponential. For CCD observations, the Anscombe transform was generalized to a mixed Gasus+Poisson noise. From the discrete wavelet transform a set of significant wavelet coefficients (SSWC)is obtained. Many applications have been derived like denoising and deconvolution. Our main application is the decomposition of the image into objects, i.e the vision. At each scale an image labelling is performed in the SSWC. An interscale graph linking the fields of significant pixels is then obtained. The objects are identified using this graph. The wavelet coefficients of the tree related to a given object allow one to reconstruct its image by a classical inverse method. This vision model has been applied to astronomical images, improving the analysis of complex structures.

Paper Details

Date Published: 27 February 2004
PDF: 8 pages
Proc. SPIE 5266, Wavelet Applications in Industrial Processing, (27 February 2004); doi: 10.1117/12.521039
Show Author Affiliations
Albert Bijaoui, Observatoire de la Cote d'Azur (France)
Eric Slezak, Observatoire de la Cote d'Azur (France)
Myriam Traina, Observatoire de la Cote d'Azur (France)


Published in SPIE Proceedings Vol. 5266:
Wavelet Applications in Industrial Processing
Frederic Truchetet, Editor(s)

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