Marble Slabs Classification System Based on Image Processing (Ark Marble Mine in Birjand)

Marble is one of the semi-precious stones that has been used in decorating building façade and making decorative things. This stone is present in the nature in the form of rock or layered stone. Examining the kind of stone, extent of impurity and different streaks in white marble is a widely confronted subject by those who are involved in this industry. Obtaining the extent of impurity of white marble using methods of detecting and analyzing material is expensive and time-consuming. In this research carried out on while marbles of Arc Mine in Birjand, it has been attempted to present very fast method using Image Processing Techniques so that while preserving identity and appearance of stone and without any damage to it, we compute the impurity level and different streaks on white marble surface. The proposed method includes two stages; in the first stage applying image processing functions, it is attempted to segment the present impurities and streaks on marble surface from the stone background and in the second stage, the area of these impurities and streaks is computed. Results obtained in this paper (97.8%) in comparison with other researches and experimental methods indicate acceptability of this algorithm

Mesures de qualité de clustering de documents : Prise en compte de la distribution des mots clés

National audienceNos travaux sur une nouvelle méthode de classification non supervisée (Germen) nous ont amenés à nous interroger sur la qualité des résultats obtenus. Le problème est d'estimer si une méthode de clustering est 'meilleure' qu'une autre pour le type de données que nous traitons (données textuelles). Dans un premier temps, après avoir fait un état de l'art des méthodes existantes, nous avons appliqué quelques indices de qualité aux résultats de clustering issus de notre algorithme Germen ainsi que d'autres algorithmes communément utilisés. Ces indices de qualité ne permettant pas de sélectionner la meilleure partition, nous avons développé une nouvelle série d'indices basés sur la distribution des mots-clés. Nous présentons et discutons les résultats obtenus ainsi que les réflexions engagées pour faire évoluer l'évaluation de classifications non supervisées sur des textes. 1 Introductio

Incremental classification of invoice documents

ISBN : 978-1-4244-2174-9International audienceThis paper deals with incremental classification and its particular application to invoice classification. An improved version of an already existant incremental neural network called IGNG (Incremental Growing Neural Gas) is used for this purpose . This neural network tries to cover the space of data by adding or deleting neurons as data is fed to the system. The improved version of the IGNG, called I2GNG used local thresholds in order to create or delete neurons. Applied on invoice documents represented with graphs, I2GNG shows a recognition rate of 97.63%

Administrative Document Analysis and Structure

International audienceThis chapter reports our knowledge about the analysis and recognition of scanned administrative documents. Regarding essentially the administrative paper flow with new and continuous arrivals, all the conventional techniques reserved to static databases modeling and recognition are doomed to failure. For this purpose, a new technique based on the experience was investigated giving very promising results. This technique is related to the case-based reasoning already used in data mining and various problems of machine learning. After the presentation of the context related to the administrative document flow and its requirements in a real time processing, we present a case based reasonning for invoice processing. The case corresponds to the co-existence of a problem and its solution. The problem in an invoice corresponds to a local structure such as the keywords of an address or the line patterns in the amounts table, while the solution is related to their content. This problem is then compared to a document case base using graph probing. For this purpose, we proposed an improvement of an already existing neural network called Incremental Growing Neural Ga