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How to set up Trac

Installation

Currently we are using Trac 11.1 installed on a Debian OS.
First you need to install ​setuptools. After you download the .egg install it with:

sudo sh setuptools-0.6c9-py2.5.egg

Then you can install Trac executing:

sudo easy_install Trac

• Usefull links

​http://pypi.python.org/pypi/setuptools#installation-instructions
​http://trac.edgewall.org/wiki/TracInstall
​http://trac.edgewall.org/wiki/TracOnDebian

Setting up Postgresql

INTRODUCTION Mathematical morphology (MM) offers a variety of tools for texture char- acterization, such as granulometry, morphological covariance, orientation maps, etc. The rst two in particular have been employed successfully in a number of texture analysis applications [3, 7, 22, 23]. More precisely, granulometry is a powerful tool based on the "sieving" principle, implemented by means of successive openings and/or closings with structuring elements (SE) of various sizes, hence it is capable of extracting shape and size characteristics from textures. Morphological covariance on the other hand, is based on erosions with pairs of points separated by vectors of various lengths, and provides information on the coarseness, anisotropy as well as periodicity of its input. In this paper, we concentrate on these two operators, and speci cally on the combined exploitation of their SE variables: size, distance and direction. Since the original size-only de nition of pattern spectra [13], these operators have been extended in various ways (e.g., color, multivariate, attribute based versions, etc.). Relatively recent applications have explored for instance the combination of SE shape and size as far as granulometry is concerned [24,25], hence leading to a feature matrix rather than a vector, that describes Proceedings of the 8th International Symposium on Mathematical Morphology, the combined size and shape distribution of its input. As to covariance, the coupled use of SE pair distance and direction makes it possible to exploit the anisotropic properties of textures additionally to their periodicity [12, 23]. Here we investigate the ways of combining the complementary infor- mation extracted by these two operators (e.g., concatenation, dimension reduction, etc.), and propose a hybrid of the two, where SE couples are varied in terms of size, direction as well as distance. The proposed combi- nation scheme is compared in terms of classi cation accuracy, against the standard de nitions, using the publically available Outex13 color texture database. The so far obtained experimental results show that it leads to an improvement over the usual concatenation of feature vectors. Furthermore, as far as the extension of this operator to color images is concerned, since MM is based on complete lattice theory, a vector ordering mechanism becomes necessary. Hence, we propose a weight based reduced vector ordering, de ned on the improved HLS (IHLS) color space, designed speci cally for the purpose of color texture classi cation. This approach makes it possible to optimize, for instance through genetic algorithms, the weight of each component adaptively, according to the training set under consideration. The rest of the paper is organized as follows. Section 2 introduces brie y granulometry and covariance, and then elaborates on the combination of their variables. In Section 3, the problem of extending morphological op- erators to multivariate images is discussed, and the proposed ordering is detailed. Next, Section 4 presents the experimental results that have been obtained with the Outex13 database. Finally, Section 5 is devoted to con- cluding remarks.