3D Model Acquisition by Tracking 2D Wireframes
This paper presents a semi-automatic wireframe acquisition system. The system uses real-time (25Hz) tracking of a user specified 2D wireframe and intermittent camera pose parameters to accumulate 3D position information. The 2D tracking framework enables the application of model-based constraints in an intuitive way which interacts naturally with the Kalman filter formulation used. In particular, this is used to introduce feedback from the current 3D shape estimate to improve the robustness of the 2D tracking. The scheme allows wireframe models of simple edge based objects to be built in around 5 minutes.

title: Recognising panoramas
abstract: The problem considered in this paper is the fully automatic construction of panoramas. Fundamentally, this problem requires recognition, as we need to know which parts of the panorama join up. Previous approaches have used human input or restrictions on the image sequence for the matching step. In this work we use object recognition techniques based on invariant local features to select matching images, and a probabilistic model for verification. Because of this our method is insensitive to the ordering, orientation, scale and illumination of the images. It is also insensitive to 'noise' images which are not part of the panorama at all, that is, it recognises panoramas. This suggests a useful application for photographers: the system takes as input the images on an entire flash card or film, recognises images that form part of a panorama, and stitches them with no user input whatsoever.

title: Unsupervised 3D object recognition and reconstruction in unordered datasets
abstract: This paper presents a system for fully automatic recognition and reconstruction of 3D objects in image databases. We pose the object recognition problem as one of finding consistent matches between all images, subject to the constraint that the images were taken from a perspective camera. We assume that the objects or scenes are rigid. For each image, we associate a camera matrix, which is parameterised by rotation, translation and focal length. We use invariant local features to find matches between all images, and the RANSAC algorithm to find those that are consistent with the fundamental matrix. Objects are recognised as subsets of matching images. We then solve for the structure and motion of each object, using a sparse bundle adjustment algorithm. Our results demonstrate that it is possible to recognise and reconstruct 3D objects from an unordered image database with no user input at all.

title: Invariant Features from Interest Point Groups
abstract: This paper approaches the problem of ¯nding correspondences between images in which there are large changes in viewpoint, scale and illumi- nation. Recent work has shown that scale-space `interest points' may be found with good repeatability in spite of such changes. Further- more, the high entropy of the surrounding image regions means that local descriptors are highly discriminative for matching. For descrip- tors at interest points to be robustly matched between images, they must be as far as possible invariant to the imaging process. In this work we introduce a family of features which use groups of interest points to form geometrically invariant descriptors of image regions. Feature descriptors are formed by resampling the image rel- ative to canonical frames de¯ned by the points. In addition to robust matching, a key advantage of this approach is that each match implies a hypothesis of the local 2D (projective) transformation. This allows us to immediately reject most of the false matches using a Hough trans- form. We reject remaining outliers using RANSAC and the epipolar constraint. Results show that dense feature matching can be achieved in a few seconds of computation on 1GHz Pentium III machines.

title: Software visualization
abstract: From the Publisher: 
In the past decade, high-quality interfaces have become standard in a growing number of areas such as games and CD-ROM-based encyclopedias. Yet the overwhelming majority of programmers edit their code using a single font within a single window and view code execution via the hand insertion of print statements. Software Visualization (SV) redresses this imbalance by using typography, graphics, and animation techniques to show program code, data, and control flow. This book describes the history of SV, techniques and frameworks for its construction, its use in education and program debugging, and recent attempts to evaluate its effectiveness. In making programming a multimedia experience, SV leaves programmers and computer science researchers free to explore more interesting issues and to tackle more challenging problems.

title: Visualization of the Interaction of Multiple Sclerosis Lesions with Adjacent White Matter Fibers Using Streamtubes and Streamsurfaces
abstract: Multiple sclerosis (MS) is a chronic disease of the central nervous system that predominantly affects young adults during their most productive years. Pathologically, MS is characterized by the presence of areas of demyelination and T-cell predominant perivascular inflammation in the brain white matter. In order to study the effect of MS lesion on the white matter, we acquire diffusion tensor imaging (DTI) data on MS patients and employ streamtubes and streamsurfaces to visualize the DTI data. We also register structural data sets with DTI data sets and generate isosurfaces of the MS lesions and the ventricles. The simultaneous visualization of all the models has the potential to elucidate the complex relationship between the MS lesions, the white matter neural fibers and the affected gray matter regions.

title: Small Subunit Ribosomal RNA Modeling Using Stochastic Context-Free Grammars
abstract: We introduce a model based on stochastic context-free grammars (SCFGs) that can construct small subunit ribosomal RNA (SSU rRNA) multiple alignments. The method takes into account both primary sequence and secondary structure basepairing interactions. We show that this method produces multiple alignments of quality close to hand edited ones and outperforms several other methods. We also introduce a method of SCFG constraints that dramatically reduces the required computer resources needed to effectively use SCFGs on large problems such as SSU rRNA. Without such constraints, the required computer resources are infeasible for most computers. This work has applications to fields such as phylogenetic tree construction.

title: Geometrically correct imagery for teleconferencing
abstract: Current camera-monitor teleconferencing applications produce unrealistic imagery and break any sense of presence for the participants. Other capture/display technologies can be used to provide more compelling teleconferencing. However, complex geometries in capture/display systems make producing geometrically correct imagery difficult. It is usually impractical to detect, model and compensate for all effects introduced by the capture/display system. Most applications simply ignore these issues and rely on the user acceptance of the camera-monitor paradigm.
This paper presents a new and simple technique for producing geometrically correct imagery for teleconferencing environments. The necessary image transformations are derived by finding a mapping between a capture and display device for a fixed viewer location. The capture/display relationship is computed directly in device coordinates and completely avoids the need for any intermediate, complex representations of screen geometry, capture and display distortions, and viewer location. We describe our approach and demonstrate it via several prototype implementations that operate in real-time and provide a substantially more compelling sense of presence than the standard teleconferencing paradigm.

title: Conformal deskewing of non-planar documents
abstract: This paper presents an approach that uses conformal mapping to parameterize a document's 3D shape to a 2D plane. Using this conformal parametrization, a restorative mapping between an image of the distorted document and a "flattened" representation of the document can be computed and used to deskew the image. Our experiments show that arbitrarily distorted documents can be restored to within a single pixel of their true planar format. In addition, surface points can be constrained to map to specified locations in the restored 2D plane.