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Introduction: The problem studied in this paper is the road insecurity, which is manifested by the big number of injuries and deaths recorded annually around the world. These victims of road accidents worry the whole community, hence the duty and the need to find solutions for reducing the number of victims and material damage. The overall purpose of this case study is to treat the problem of corporeal accidents known in France. Case description: The case study presented in this paper is intended to help decisionmakers to find and understand the all significant relationships and correlations that exist between the conditions that led to these corporeal accidents. In fact, the two French ministries of interior and transport have jointly created a unique database on corporeal accidents, called BAAC "Accident Analysis Bulletin Corporal", with the aim of allowing different exploitations of this database by different concerned administrations and research organizations. Our intervention consists to adopt a hybrid approach based on data mining techniques combined to the multicriteria decision methods. This approach allows to extract the most relevant association rules. The results thus obtained can be easily exploited by the decision-makers to choose the appropriate policies in the perspective of improving road safety. Discussion and evaluation: The proposed approach ranks all association rules in order of importance for several quality measures of association rules. The alternatives at the top of the ranking are the results to retaining for the analysis. The approach is applied to the BAAC database of 2016, which led to the selection of three association rules. These association rules reveal that there are narrow correlations between the following elements: Driver with Pedestrian, Normal Surface of Road with Normal Atmospheric Condition and the Pavement with Pedestrian. These correlations can be justified by excess speed and carelessness of the drivers. Conclusion: The improvement of the road safety needs mainly to work more intensively on the behavioral side of the road users. For future work, it is planned to apply the proposed approach to the French traffic accident database, containing all the data on road accidents collected over several years. In addition, other measures will be used in the ranking of the association rules.

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Research investigating the use of Legendre moments for pattern recognition has been performed in recent years. This field of research remains quite open. This paper proposes a new technique based on block-based reconstruction method (BBRM) using Legendre moments compared with the global reconstruction method (GRM). For alleviating the blocking artifact involved in the processing, we propose a new approach using lapped block-based reconstruction method (LBBRM). For the problem of selecting the optimal number of moment used to represent a given image, we propose the maximum entropy principle (MEP) method. The main motivation of the proposed approaches is to allow fast and efficient reconstruction algorithm, with improvement of the reconstructed images quality. A binary handwritten musical character and multi-gray-level Lena image are used to demonstrate the performance of our algorithm