This paper describes a data processing application allowing the handling and the visualization of analytical data obtained from the chemical analyses on natural dyes identified in art objects selected in the context of the Euro-Mediterranean MedColourTech project. To provide user with functionalities for handling chemical content, we integrate and exploit existing open sources plugins of the Bioclipse platform. Then, and for specific needs of the MedColourTech project, we implement a new plugins allowing the management and the handling of information relating to the art objects, dyes identified in the samples, coloring compounds and their biological sources... etc. The rich functionality, intuitive user interface, portability, extensibility and powerful plugin architecture make MedColourTech application an ideal workbench for handling natural organic dyes.

In this article we describe an approach to the massive production of digital images of manuscripts to be published on the Internet. The goal of this work is to offer an easy-to-use interface that allows customizable views of images manuscript in several sizes and provide searching and browsing functionality for users of arabic manuscripts using an XML representation for metadata and annotation of manuscript that uses the emerging Text Encoding Initiative (TEI) standard from W3C for the representation of manuscripts.

In this paper we propose a Genetic-Based Inverse Voting Hough Transform (GBIVHT) method for detecting parabolic shapes iii B-scan images obtained by the ultrasonic Time Of Flight Diffraction inspection technique. These parabolic shapes are characteristics of the presence of crack defects in the engineering structure under inspection. In our method, the local peak detection problem in the parameters space of conventional Hough Transform is converted into a parameter optimization problem that operates directly on the ultrasound B-scan image. The optimization is done using the well-known Genetic Algorithms. Our main goals are an accurate detection of the parabolas while circumventing the computational complexity and huge storage problem tied to conventional Hough Transform.

Increasing the search speed for matching range and domain blocs is the main challenge facing fractal-based images compression. One way to remedy at this problem is to classify image blocs into categories and only search among domain blocs which are in the same category as the target range bloc. Since image blocs with a simple edge are a very important portions of the perceptual information content in image, we propose a method to both identify and classify this kind of blocs according to their edge presentation. We refer to this method as forced classification. This method is combined with other suitable methods of blocs classification available in the literature to allow a fast and encoding of grey-scale images. The result obtained is good, the encoding time for a 512/spl times/512 image is reduced by a factor of 37.52% than using the Fisher classification only, while the loss of image quality is low.