Incorporating Prior Knowledge Into Image Registration.
The first step in the spatial normalization of brain images is usually to determine the affine transformation that best maps the image to a template image in a standard space. We have developed a rapid and automatic method for performing this registration, which uses a Bayesian scheme to incorporate prior knowledge of the variability in the shape and size of heads. We compared affine registrations with and without incorporating the prior knowledge. We found that the affine transformations derived using the Bayesian scheme are much more robust and that the rate of convergence is greater.
With the development of internet and storage devices, online document servers abound with enormous quantities of documents, so that finding the right and useful information becomes a very difficult task. The end user, generally overloaded by information, can’t efficiently perceive such information. It became urgent to propose new information retrieval systems able to apprehend efficiently these enormous quantities of documents. In this paper we present PQIR an information retrieval model based on user profile. The originality of our approach is a choice of indexing terms depending on the user request but also on his profile. An empirical study confirms the relevance of our approach.