Shape recognition and pose estimation for mobile Augmented Reality

Nestor is a real-time recognition and camera pose estimation system for planar shapes. The system allows shapes that carry contextual meanings for humans to be used as Augmented Reality (AR) tracking targets. The user can teach the system new shapes in real time. New shapes can be shown to the system frontally, or they can be automatically rectified according to previously learned shapes. Shapes can be automatically assigned virtual content by classification according to a shape class library. Nestor performs shape recognition by analyzing contour structures and generating projective-invariant signatures from their concavities. The concavities are further used to extract features for pose estimation and tracking. Pose refinement is carried out by minimizing the reprojection error between sample points on each image contour and its library counterpart. Sample points are matched by evolving an active contour in real time. Our experiments show that the system provides stable and accurate registration, and runs at interactive frame rates on a Nokia N95 mobile phone.     

Target oriented network intelligence collection: effective exploration of social networks

World Wide Web - Target Oriented Network Intelligence Collection (TONIC) is a crawling process whose goal is to find social network profiles that contain information about a given target. Such...     

Carving for topology simplification of polygonal meshes

The topological complexity of polygonal meshes has a large impact on the performance of various geometric processing algorithms, such as rendering and collision detection algorithms. Several approaches for simplifying topology have been discussed in the literature. These methods operate locally on models, which makes their effect on the topology hard to predict and analyze. Most existing methods tend to exhibit several disturbing artifacts, such as shrinking of the input and splitting of its components. We propose a novel top-down approach for topology simplification that avoids most problems that are common in existing methods. We start with a simple, genus-zero mesh that bounds the input and gradually introduce topologic features by a series of carving operations. This process yields a multiresolution stream of meshes with increasing topologic level of detail. We further present a carving algorithm that is based on constrained Delaunay tetrahedralization. The algorithm first constructs the tetrahedral mesh of the complement of the input with respect to its convex hull. It then proceeds to eliminate tetrahedra in a prioritized manner. We present quality results for two families of meshes that are difficult to simplify by all existing methods known to us: topologically complex meshes and highly clustered meshes.     

In-Place Augmented Reality

In this paper, we present a vision-based approach for transmitting virtual models for Augmented Reality, which we name In-Place Augmented Reality (IPAR). A two-dimensional representation of the virtual models is embedded in a printed image. We apply computer vision techniques to interpret the printed image and extract the virtual models, which are then overlaid on the printed image. The main advantages of our approach are: (1) the image of the embedded virtual models and their behaviors are understandable to a human without using an AR system and (2) no database or network communication is required to retrieve the models. To demonstrate the technology and test its usability, we implemented several applications and performed a user evaluation. We discuss how the proposed technique can be used for the development of applications in different domains such as education, advertisement, and gaming.