Security and trust are core research issues for the further development of the Information Society and for 10 years have played, and continue to play, an integral part in the European Union’s Framework Programmes (FPs) for R&D. EU-supported collaborative research projects in trust and security bring together multi-partner stakeholders from industry (technology and service providers, system integrators and end-users), academic and research laboratories working in several interdisciplinary research fields. Sometimes, projects include actors from the legal, social and economic sectors. Together their joint efforts permit a better understanding of the conflicts and synergies between security, privacy and free market economics, as well as of the psychology and sociology of trust and security when building and deploying new technologies. The long term goal for funding this research effort is to convert the know-how of the EU in security, privacy and trust into economic advantages. In the period 1998–2002, under FP5, original and ground-breaking scientific & technological (S&T) work took place in ICT Trust and Security. Key S&T developments achieved at that period included significant advances in cryptology, smart cards and biometrics. EU-supported research also permitted the identification of new concepts in fields of work such as privacy, dependability and risk analysis. In the period 2002–2006, under FP6, research efforts in ICT security and trust have been further intensified. As part of the FP6-IST Programme, 37 R&D projects

In this paper, the authors describe a hierarchical framework for the computation of displacement fields from a sequence of images acquired by a moving camera and containing several moving objects. They also describe a matching algorithm which is consistent with the proposed framework and they represent some experimental results demonstrating the robustness of their approach.

The authors describe a hierarchical framework for the computation of displacement fields from a sequence of images acquired by a moving camera and containing several moving objects. They also describe a matching algorithm which is consistent with the proposed framework and present some experimental results demonstrating the robustness of the approach.>

The problem of determining the intrinsic and extrinsic parameters of a mobile camera is addressed. An optimal solution which consists of the following steps is presented. First, the camera is calibrated in several working positions and, for each position, the corresponding transformation matrix is computed using a method developed by O.D. Faugeras and G. Toscani (1987). Next, optimal intrinsic parameters are searched for all positions. Finally, for each separate position, optimal extrinsic parameters are computed by minimizing a mean square error through a closed-form solution. Experimental results show that such a technique yields a very large reduction of calibration errors and a considerable gain relative to other existing on-site calibration techniques.>

The authors propose a method for solving the stereo correspondence problem. The method consists of extracting local image structures and matching similar such structures between two images. Linear edge segments are extracted from both the left and right images. Each segment is characterized by its position and orientation in the image as well as its relationships with the nearby segments. A relational graph is thus built from each image. For each segment in one image as set of potential assignments is represented as a set of nodes in a correspondence graph. Arcs in the graph represent compatible assignments established on the basis of segment relationships. Stereo matching becomes equivalent to searching for sets of mutually compatible nodes in this graph. Sets are found by looking for maximal cliques. The maximal clique best suited to represent a stereo correspondence is selected using a benefit function. Numerous results obtained with this method are shown.>

An approach for solving the stereo correspondence problem is suggested that is based on extracting local image structures and matching similar structures between two images. The individual matches are represented as nodes in a graph. Arcs in this graph represent local photometric, geometrical, and topological similarities between the two images. Left/right correspondences are equivalent with sets of mutually compatible nodes, which are found as maximal cliques in the graph. The best clique selected according to an evaluation procedure is considered to be the best available match. An algorithm based on this approach is described and evaluated. Some experimental results obtained by applying this algorithm to a pair of images representing an indoor scene are reported.>