Multi-image matching using multi-scale oriented patches
This paper describes a novel multi-view matching framework based on a new type of invariant feature. Our features are located at Harris corners in discrete scale-space and oriented using a blurred local gradient. This defines a rotationally invariant frame in which we sample a feature descriptor, which consists of an 8 /spl times/ 8 patch of bias/gain normalised intensity values. The density of features in the image is controlled using a novel adaptive non-maximal suppression algorithm, which gives a better spatial distribution of features than previous approaches. Matching is achieved using a fast nearest neighbour algorithm that indexes features based on their low frequency Haar wavelet coefficients. We also introduce a novel outlier rejection procedure that verifies a pairwise feature match based on a background distribution of incorrect feature matches. Feature matches are refined using RANSAC and used in an automatic 2D panorama stitcher that has been extensively tested on hundreds of sample inputs.

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: 3D Model Acquisition by Tracking 2D Wireframes
abstract: 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: 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: Beyond 2D images: effective 3D imaging for library materials
abstract: Significant efforts are being made to digitize rare and valuable library materials, with the goal of providing patrons and historians digital facsimiles that capture the "look and feel" of the original materials. This is often done by digitally photographing the materials and making high resolution 2D images available. The underlying assumption is that the objects are flat. However, older materials may not be flat in practice, being warped and crinkled due to decay, neglect, accident and the passing of time. In such cases, 2D imaging is insufficient to capture the "look and feel" of the original. For these materials, 3D acquisition is necessary to create a realistic facsimile. This paper outlines a technique for capturing an accurate 3D representation of library materials which can be integrated directly into current digitization setups. This will allow digitization efforts to provide patrons with more realistic digital facsimile of library materials.

title: Literate Programming Applied to Conventional Software Design

title: A Hypertext for Literate Programming
abstract: In this paper we describe a hypertext presentation for the WEB system for literate programming. The requirements for an environment for modern literate programming are analyzed and the WEB hypertext system is proposed. Different types of windows for displaying text, indices and graphical representations are discussed. All semantic links and operators using them are analyzed. The proposed architecture of the WEB hypertext system includes a general purpose relational database management system. Mapping between hypertext queries and relational database queries is provided. The system allows the user to define new hypertext operations by providing the corresponding SQL queries for the underlying relational database. The described system can be easily extended by many other types of queries and applications.

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.

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.