Blow-up results for semilinear wave equations in the super-conformal case

We consider the semilinear wave equation in higher dimensions with power nonlinearity in the super-conformal range, and its perturbations with lower order terms, including the Klein-Gordon equation. We improve the upper bounds on blow-up solutions previously obtained by Killip, Stovall and Vi\c{s}an [6]. Our proof uses the similarity variables' setting. We consider the equation in that setting as a perturbation of the conformal case, and we handle the extra terms thanks to the ideas we already developed in [5] for perturbations of the pure power case with lower order terms

A case-based reasoning approach for unknown class invoice processing

International audienceThis paper introduces an invoice analysis approach using Case-Based Reasoning (CBR). CBR is used to analyze and interpret new invoices thanks to the previous processing experiences. Each new document is segmented into structures and interpreted thanks to a structure database. Interpreting a new document's structures relies on graph edit distance as well as on string edit distance. This paper focuses on document structure extraction as well as on document interpretation via its structures interpretation. The proposed system reaches an extraction and interpretation rate of 76.33%

A case-based reasoning approach for invoice structure extraction

ISBN : 978-0-7695-2822-9International audienceThis paper shows the use of case-based reasoning (CBR) for invoice structure extraction and analysis. This method, called CBR-DIA (CBR for Document Invoice Analysis), is adaptive and does not need any previous training. It analyses a document by retrieving and analysing similar documents or elements of documents (cases) stored in a database. The retrieval step is performed thanks to graph comparison techniques like graph probing and edit distance. The analysis step is done thanks to the information found in the nearest retrieved cases. Applied on 950 invoices, CBR-DIA reaches a recognition rate of 85.29% for documents of known classes and 76.33% for documents of unknown classes

Case-based reasoning for invoice analysis and recognition

The original publication is available at www.springerlink.com , ISBN 978-3-540-74138-1, ISSN 0302-9743 (Print) 1611-3349 (Online)International audienceThis paper introduces the approach CBRDIA (Case-based Reasoning for Document Invoice Analysis) which uses the principles of case-based reasoning to analyze, recognize and interpret invoices. Two CBR cycles are performed sequentially in CBRDIA. The first one consists in checking whether a similar document has already been processed, which makes the interpretation of the current one easy. The second cycle works if the first one fails. It processes the document by analyzing and interpreting its structuring elements (adresses, amounts, tables, etc) one by one. The CBR cycles allow processing documents from both knonwn or unknown classes. Applied on 923 invoices, CBRDIA reaches a recognition rate of 85,22% for documents of known classes and 74,90% for documents of unknown classes

Nouveau modèle de texture markovien basé sur la loi K : Application à l'echographie

- On propose dans cet article une nouvelle modélisation de la texture issue de l'imagerie cohérente basée sur les champs de Markov. Ce modèle permet de prendre en compte le comportement spatial de la texture tout en conservant une distribution locale de type loi K. Afin de ce respecter la nature des intensités lumineuses, on choisit de se placer localement dans le cadre du modèle produit. Ainsi, la construction de ce modèle passe par celle d'un champ markovien de distribution locale gamma. Ce dernier modèle est volentairement choisi en dehors de la classe des auto-modèles de Besag afin de pouvoir contrôler façilement sa moyenne locale. Afin de comprendre le rôle des paramètres du champ markovien K, un ensemble de textures synthétiques sont ici présentées. On procède ensuite à une première validation du modèle par la synthèse de textures échographiques. Ce modèle est ensuite testé sur des textures extraites d'images échographiques

An end-to-end administrative document analysis system

International audienceThis paper presents an end-to-end administrative document analysis system. This system uses case-based reasoning in order to process documents from known and unknown classes. For each document, the system retrieves the nearest processing experience in order to analyze and interpret the current document. When a complete analysis is done, this document needs to be added to the document database. This requires an incremental learning process in order to take into account every new information, without losing the previous learnt ones. For this purpose, we proposed an improved version of an already existing neural network called Incremental Growing Neural Gas. Applied on documents learning and classification, this neural network reaches 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