Showing papers by "Andrew Zisserman published in 1996"

3D Model Acquisition from Extended Image Sequences

Abstract: A method for matching image primitives through a sequence is described, for the purpose of acquiring 3D geometric models. The method includes a novel robust estimator of the trifocal tensor, based on a minimum number of token correspondences across an image triplet; and a novel tracking algorithm in which corners and line segments are matched over image triplets in an integrated framework. The matching techniques are both robust (detecting and discarding mismatches) and fully automatic.

Motion Deblurring and Super-resolution from an Image Sequence

Abstract: In many applications, like surveillance, image sequences are of poor quality. Motion blur in particular introduces significant image degradation. An interesting challenge is to merge these many images into one high-quality, estimated still. We propose a method to achieve this. Firstly, an object of interest is tracked through the sequence using region based matching. Secondly, degradation of images is modelled in terms of pixel sampling, defocus blur and motion blur. Motion blur direction and magnitude are estimated from tracked displacements. Finally, a high-resolution deblurred image is reconstructed. The approach is illustrated with video sequences of moving people and blurred script.

Self-Calibration from Image Triplets

Abstract: We describe a method for determining affine and metric calibration of a camera with unchanging internal parameters undergoing planar motion. It is shown that affine calibration is recovered uniquely, and metric calibration up to a two fold ambiguity.

Goal-directed Video Metrology

Abstract: We investigate the general problem of accurate metrology from uncalibrated video sequences where only partial information is available. We show, via a specific example — plotting the position of a goal-bound soccer ball — that accurate measurements can be obtained, and that both qualitative and quantitative questions about the data can be answered.

Robust Parameterization and Computation of the Trifocal Tensor.

Abstract: This paper presents all algorithm for computing a maximum likelihood estimate (MLE) of the trifocal tensor. The input to the algorithm is three images of the same scene, and the output is the estimated tensor and corner and line feature matches across the three images that are consistent with this estimate. Particular novelties of the algorithm are the computation of a trifocal tensor from six point correspondences, and a parameterization of the trifocal tensor which enforces the constraints between the tensor elements. The algorithm uses techniques from robust statistics and is fully automatic. Results are presented for synthetic and real image triplets. The proposed parameterization is compared to other existing methods.

Visualising Cerebral Asymmetry

Abstract: We describe techniques for visualising and measuring the asymmetry of the cerebral hemispheres based on MRI scans. These techniques improve on previous approaches in two ways: firstly, measurements are not limited to voxel discretisation scales; secondly, symmetry measurements are inherently 3D. This avoids the errors in slice-based measurements arising from shape distortions introduced by misalignment between the head and MRI machine.

Semi-local projective invariants for the recognition of smooth plane curves

Abstract: Recently, several methods have been proposed for describing plane, non-algebraic curves in a projectively invariant fashion. These curve representations are invariant under changes in viewpoint and therefore ideally suited for recognition. We report the results of a study where the strengths and weaknesses of a number of semi-local methods are compared on the basis of the same images and edge data. All the methods define a distinguished or canonical projective frame for the curve segment which is used for projective normalisation. In this canonical frame the curve has a viewpoint invariant signature. Measurements on the signature are invariants. All the methods presented are designed to work on real images where extracted data will not be ideal, and parts of curves will be missing because of poor contrast or occlusion. We compare the stability and discrimination of the signatures and invariants over a number of example curves and viewpoints. The paper concludes with a discussion of how the various methods can be integrated within a recognition system.

An Experimental Comparison of Appearance and Geometric Model Based Recognition

Abstract: This paper describes an experimental investigation of the recognition performance of two approaches to the representation of objects for recognition. The first representation, generally known as appearance modelling, describes an object by a set of images. The image set is acquired for a range of views and illumination conditions which are expected to be encountered in subsequent recognition. This image database provides a description of the object. Recognition is carried out by constructing an eigenvector space to compute efficiently the distance between a new image and any image in the database. The second representation is a geometric description based on the projected boundary of an object. General object classes such as planar objects, surfaces of revolution and repeated structures support the construction of invariant descriptions and invariant index functions for recognition.

Object representation in computer vision II : ECCV '96 International Workshop, Cambridge, U.K., April 13-14, 1996 : proceedings

Abstract: Report on the 1996 international workshop on object representation in computer vision.- From an intensity image to 3-D segmented descriptions.- Recovering Generalized Cylinders by monocular vision.- Combinatorial geometry for shape representation and indexing.- Representing objects using topology.- Curvature based signatures for object description and recognition.- On 3D shape synthesis.- Shape constancy in pictorial relief.- Dimensionality of illumination manifolds in appearance matching.- Learning object representations from lighting variations.- Learning appearance models for object recognition.- An image oriented CAD approach.- An experimental comparison of appearance and geometric model based recognition.- Virtualized reality: Being mobile in a visual scene.- Generic shape learning and recognition.- A hybrid approach to 3D representation.- Finding pictures of objects in large collections of images.- Beyond the Hough transform: Further properties of the R? mapping and their applications.- Non-Euclidean object representations for calibration-free video overlay.