Abstract
We have implemented multiscale vision models based on the wavelet transform to analyze field astronomical images. The discrete transform is performed by the à trous or the pyramidal algorithms. The vision models are based on the notion of the significant structures. Different kind of noises have beeen taken into account. We identify the pixels of the wavelet transform space (WTS) associated with the objects. At each scale a region labelling is carried out. An interscale connectivity graph is then established. In accordance with some rules that permit false detections to be removed, the objects and their sub-objects are identified. They define respectively trees and sub-trees in the graph. So, the identification of the WTS pixels of the tree related to a given object leads to the reconstruction of its image by the conjugate gradient method. The model has been tested successfully on astronomical images which shows that complex structures are better analyzed than using usual astronomical vision models.
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Bijaoui, A., Rué, F., Savalle, R. (1997). Wavelet Transform and Multiscale Vision Models. In: Babu, G.J., Feigelson, E.D. (eds) Statistical Challenges in Modern Astronomy II. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1968-2_10
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DOI: https://doi.org/10.1007/978-1-4612-1968-2_10
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