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International audience; Handwriting recognition systems are typically trained using publicly available databases, where data have been collected in controlled conditions (image resolution, paper background, noise level, etc.). Since this is not often the case in real-world scenarios, classification performance can be affected when novel data is presented to the word recognition system. To overcome this problem, we present in this paper a new approach called database adaptation. It consists of processing one set (training or test) in order to adapt it to the other set (test or training, respectively). Specifically, two kinds of preprocessing, namely stroke thickness normalization and pixel intensity normalization are considered. The advantage of such approach is that we can re-use the existing recognition system trained on controlled data. We conduct several experiments with the Rimes 2011 word database and with a real-world database. We adapt either the test set or the training set. Results show that training set adaptation achieves better results than test set adaptation, at the cost of a second training stage on the adapted data. Accuracy of data set adaptation is increased by 2% to 3% in absolute value over no adaptation.

We address the problem of defining degrees of coherence for partially polarized light. We discuss a new theoretical approach that introduces intrinsic degrees of coherence. These parameters make it possible to separate partial polarization and partial coherence and are measurable quantities. We analyze their physical meaning and propose a method to measure them with a finite number of measurement.

The GALEX mission of NASA, is collecting an unprecedent set of astronomical UV data in the far and the near UV range. The telescope measures the full sky in a continuous automatic scan. Knowing the attitude data, local images are simultaneously extracted and corrected for smearing and instrumental effects. Final UV images show, by far, a lower resolution than their visible counterpart. It originates blends, ambiguities and missidentifications of the astronomical sources. Our purpose is to deduce from the UV image the UV photometry of the visible objets through a bayesian approach, using the visible data (catalog and image) as the starting reference for the UV analysis. For the feasibility reasons as the deep field images are very large, a segmentation procedure has been defined to manage the analysis in a tractable form. The present paper discusses all these aspects and details the full method and performances.

A new snake based technique for target segmentation is presented. This approach can be implemented with optical correlators and thus enlarge their field of application.