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Over the five year period, this project has mainly been concerned about developing a theory for statistical analysis of shapes of objects, both two and three-dimensional. Focusing on the boundaries of these objects, our framework is for shape analysis of curves and surfaces. The main achievements were development of tools for: (1) Quantifying Shape Differences: Given any two objects, we can quantify differences between their shapes. (2) Achieve Desired Invariance: Our notion of shape is invariant to certain transformations of curves - rigid motion, scaling and re-parameterization. (3) Compute Summary Statistics: Given a collection of shapes and shape classes we can generate summary statistics - mean, covariance, etc, to characterize a shape class. (4) Stochastic Modeling: We have developed probability models that capture observed variability in shape classes. These models form priors for Bayesian inferences. (5) Statistical Inferences: We have studied statistical evaluations, such as hypothesis testing, likelihood ratios, performance bounds, etc, for shape analysis. The salient components of this differential geometric framework are following. First, we define a space of curves or surfaces by choosing a mathematical representation for these objects and establish a Hilbert submanifold(s) for such representations. Then, we choose a Riemannian metric, usually an elastic metric for measuring distances on such manifolds. We arrive at a shape manifold by imposing remaining invariances in the representation. For these shape spaces, we have developed two numerical techniques for computing geodesic paths. Finally, we define and compute empirical statistics, and define probability models on tangent bundles. The resulting statistical models are then used to characterize objects in images according to shapes, for using in object detection, tracking and recognition. We have demonstrated these tools in different application understanding

Acinetobacter baumannii is a bacterial pathogen of increasing medical significance in military and civilian healthcare facilities. The importance of A. baumannii infections in war-related injuries is well established. A. baumannii was the most common gram-negative bacillus recovered from traumatic injuries to the lower extremities during the Vietnam War. More recently a new series of infections was reported in U.S. service personnel injured in the Iraq/Kuwait/Afghanistan regions during Operation Iraqi Freedom and in Afghanistan during Operation Enduring Freedom and many of these isolates expressed a high degree of antimicrobial resistance. In addition, the incidence of multidrug-resistant (MDR) A. baumannii infections, associated with significant morbidity and mortality, are increasing worldwide. Thus there is a critical need to identify potential vaccine antigens and new drug targets expressed by A. baumannii. In the past year we have made significant progress in our research. We identified three surface polysaccharides and three proteins that may serve as vaccine candidates and/or drug targets. We have developed an animal model that will allow us to test the se bacterial components in vivo. Finally, we completely sequenced and annotated the genome of A. baumannii 307, which allows us to search for novel targets in silico.