Digital Identity Management 

About: Digital Identity Management is an academic conference. The conference publishes majorly in the area(s): Image registration & Iterative reconstruction. Over the lifetime, 486 publications have been published by the conference receiving 18390 citations.

Efficient variants of the ICP algorithm

Abstract: The ICP (Iterative Closest Point) algorithm is widely used for geometric alignment of three-dimensional models when an initial estimate of the relative pose is known. Many variants of ICP have been proposed, affecting all phases of the algorithm from the selection and matching of points to the minimization strategy. We enumerate and classify many of these variants, and evaluate their effect on the speed with which the correct alignment is reached. In order to improve convergence for nearly-flat meshes with small features, such as inscribed surfaces, we introduce a new variant based on uniform sampling of the space of normals. We conclude by proposing a combination of ICP variants optimized for high speed. We demonstrate an implementation that is able to align two range images in a few tens of milliseconds, assuming a good initial guess. This capability has potential application to real-time 3D model acquisition and model-based tracking.

Multiview registration for large data sets

Abstract: We present a multiview registration method for aligning range data. We first align scans pairwise with each other and use the pairwise alignments as constraints that the multiview step enforces while evenly diffusing the pairwise registration errors. This approach is especially suitable for registering large data sets, since using constraints from pairwise alignments does not require loading the entire data set into memory to perform the alignment. The alignment method is efficient, and it is less likely to get stuck into a local minimum than previous methods, and can be used in conjunction with any pairwise method based on aligning overlapping surface sections.

OpenID 2.0: a platform for user-centric identity management

Abstract: With the advancement in user-centric and URI-based identity systems over the past two years, it has become clear that a single specification will not be the solution to all problems. Rather, like the other layers of the Internet, developing small, interoperable specifications that are independently implementable and useful will ultimately lead to market adoption of these technologies. This is the intent of the OpenID framework. OpenID Authentication 1.0 began as a lightweight HTTP-based URL authentication protocol. OpenID Authentication 2.0 it is now turning into an open community-driven platform that allows and encourages innovation. It supports both URLs and XRIs as user identifiers, uses Yadis XRDS documents for identity service discovery, adds stronger security, and supports both public and private identifiers. With continuing convergence under this broad umbrella, the OpenID framework is emerging as a viable solution for Internet-scale user-centric identity infrastructure.

Silhouette and stereo fusion for 3D object modeling

Abstract: We present a new approach to high quality 3D object reconstruction. Starting from a calibrated sequence of color images, the algorithm is able to reconstruct both the 3D geometry and the texture. The core of the method is based on a deformable model, which defines the framework where texture and silhouette information can be fused. This is achieved by defining two external forces based on the images: a texture driven force and a silhouette driven force. The texture force is computed in two steps: a multistereo correlation voting approach and a gradient vector flow diffusion. Due to the high resolution of the voting approach, a multigrid version of the gradient vector flow has been developed. Concerning the silhouette force, a new formulation of the silhouette constraint is derived. It provides a robust way to integrate the silhouettes in the evolution algorithm. As a consequence, we are able to recover the apparent contours of the model at the end of the iteration process. Finally, a texture map is computed from the original images for the reconstructed 3D model.

Geometrically stable sampling for the ICP algorithm

Abstract: The iterative closest point (ICP) algorithm is a widely used method for aligning three-dimensional point sets. The quality of alignment obtained by this algorithm depends heavily on choosing good pairs of corresponding points in the two datasets. If too many points are chosen from featureless regions of the data, the algorithm converges slowly, finds the wrong pose, or even diverges, especially in the presence of noise or miscalibration in the input data. We describe a method for detecting uncertainty in pose, and we propose a point selection strategy for ICP that minimizes this uncertainty by choosing samples that constrain potentially unstable transformations.