One of the challenges in 3D shape matching arises from the fact that in many applications, models should be considered to be the same if they differ by a rotation. Consequently, when comparing two models, a similarity metric implicitly provides the measure of similarity at the optimal alignment. Explicitly solving for the optimal alignment is usually impractical. So, two general methods have been proposed for addressing this issue: (1) Every model is represented using rotation invariant descriptors. (2) Every model is described by a rotation dependent descriptor that is aligned into a canonical coordinate system defined by the model. In this paper, we discuss the limitations of canonical alignment and present a new mathematical tool, based on spherical harmonics, for obtaining rotation invariant representations. We describe the properties of this tool and show how it can be applied to a number of existing, orientation dependent, descriptors to improve their matching performance. The advantage of this is twofold: First, it improves the matching performance of many descriptors. Second, it reduces the dimensionality of the descriptor, providing a more compact representation, which in turn makes comparing two models more efficient.

Many ad hoc network protocols and applications assume the knowledge of geographic location of nodes. The absolute position of each networked node is an assumed fact by most sensor networks which can then present the sensed information on a geographical map. Finding position without the aid of GPS in each node of an ad hoc network is important in cases where GPS is either not accessible, or not practical to use due to power, form factor or line of sight conditions. Position would also enable routing in sufficiently isotropic large networks, without the use of large routing tables. We are proposing APS -- a localized, distributed, hop by hop positioning algorithm, that works as an extension of both distance vector routing and GPS positioning in order to provide approximate position for all nodes in a network where only a limited fraction of nodes have self positioning capability.

Trajectory based forwarding (TBF) is a novel method to forward packets in a dense ad hoc network that makes it possible to route a packet along a predefined curve. It is a generalization of source based routing and Cartesian forwarding in that the trajectory is set by the source, but the forwarding decision is based on the relationship to the trajectory rather than the final destination. The fundamental aspect of TBF is that it decouples path naming from the actual path, thereby providing a common framework for applications such as: flooding, unicast, multicast and multipath routing, and discovery in ad hoc networks. TBF requires that nodes know their position relative to a coordinate system. While a global coordinate system a#orded by a system such as GPS would be ideal, in this paper we propose Local Positioning System (LPS), a method that only positions the nodes along the trajectory, by making use of other node capabilities, such as angle of arrival or range estimations, compasses and accelerometers. We explore several forwarding strategies that are appropriate for these node capabilities.

This paper reports a novel method for fully automated segmentation that is based on description of shape and its variation using Point Distribution Models (PDM). An improvement of the Active Shape procedure introduced by Cootes and Taylor to find new examples of previously learned shapes using PDMs is presented. The new method for segmentation and interpretation of deep neuroanatomic structures such as thalamus, putamen, ventricular system, etc. incorporates a priori knowledge about shapes of the neuroanatomic structures to provide their robust segmentation and labeling in MR brain images. The method was trained in 8 MR brain images and tested in 19 brain images by comparison to observer-defined independent standards. Neuroanatomic structures in all testing images were successfully identified. Computer-identified and observer-defined neuroanatomic structures agreed well. The average labeling error was 7 \Sigma 3%. Border positioning errors were quite small, with the average border posi...

this paper are fast, accurate, and robust in the face of noise and degeneracies. The implementation tracks accurately for thousands of frames in low-res low-quality video, giving results that appear to compare favorably with the state-of-the-art. We are now studying more interesting camera models and the problem of integrating over uncertainty through time

... of 3D models, the need for retrieval of models has gained prominence in the graphics and vision communities. A variety of methods have been proposed that enable the efficient querying of model repositories for a desired 3D shape. Many of these methods use a 3D model as a query and attempt to retrieve models from the database that have a similar shape. In this paper

We provide in this article a generic framework for pose estimation from geometric features. We propose more particularly two algorithms: a gradient descent on the Riemannian least squares distance and on the Mahalanobis distance. For each method, we provide a way to compute the uncertainty of the resulting transformation. The analysis and comparison of the algorithms show their advantages and drawbacks and point out the very good prediction on the transformation accuracy. An application in medical image analysis validates the uncertainty estimation on real data and demonstrates that, using adapted and rigorous tools, we can detect very small modiøcations in medical images. We believe that these algorithms could be easily embedded in many applications and provide a thorough basis for computing many image statistics.

Recently 3 algorithms for registration of multiple partially overlapping point sets have been published by Pennec [11], Stoddart & Hilton [12] and Benjemma & Schmitt [1]. The problem is of particular interest in the building of surface models from multiple range images taken from several viewpoints.

The contribution of the paper is two-fold: Firstly, a review of the point set registration literature is given, and secondly a novel weighted least squares formulation of the point set registration problem is presented. Numerical registration results, produced by registering 3D point correspondence data sets using the weighted least squares approach show improved accuracy over those produced by an unweighted least squares formulation for cases where the point measurements are heteroscedastically and anisotropically distributed. 1

With recent improvements in methods for the acquisition and rendering of 3D models, the need for retrieval of models from large repositories of 3D shapes has gained prominence in the graphics and vision communities. A variety of methods have been proposed that enable the efficient querying of model repositories for a desired 3D shape. Many of these methods use a 3D model as a query and attempt to retrieve models from the database that have a similar shape.