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In recent years, cities are experiencing a deep negative impact due to traffic congestion. As the number of vehicles is increasing rapidly, traffic congestion becomes unsustainable in urban domains specially in peak hours leading to time and fuel waste, accidents, additional costs for the economy, environmental problems and consequently impaired quality of life. Tremendous effort is done to provide solutions dealing with traffic congestion detection and monitoring. Based on used technologies, existing solutions can be classified into three categories: sensor technology, Vehicular Adhoc Networks and visual traffic surveillance. In this survey, we review the main recent works proposed in each category and discuss their strengths and weaknesses. We give an insight of future directions for handling congestion using the presented methods.

Segmentation and volume measurement of liver tumor are important tasks for surgical planning and cancer follow-up. In this work, a segmentation method from four-phase computed tomography images is proposed. It is based on the combination of the Expectation-Maximization algorithm and the Hidden Markov Random Fields. The latter considers the spatial information given by voxel neighbors of two contrast phases. The segmentation algorithm is applied on a volume of interest that decreases the number of processed voxels. To accelerate the classification steps within the segmentation process, a Bootstrap resampling scheme is also adopted. It consists in selecting randomly an optimal representative set of voxels. The experimental results carried out on three clinical datasets show the performance of our liver tumor segmentation method. It has been notably observed that the computing time of the classification algorithm is reduced without any significant impact on the segmentation accuracy.