This work investigates the propagation of errors from the camera motion to the epipolar constraint. A relation between the perturbation of the motion parameters and the error in the epipolar constraint is derived. Based on this relation, the sensitivity of the motion parameters to the epipolar constraint is characterised, and a constraint on the allowed perturbation in a motion parameter in response to a threshold for the produced error in the epipolar constraint is determined. The presented error propagation model is useful to vision systems such as a mobile robot where the camera motion is provided by an odometry sensor. Experimental results on real images are presented.

In this paper we present a geometric theory for reconstruction of surface models from sparse 3D data captured from N camera views which are consistent with the data visibility. Sparse 3D measurements of real scenes are readily estimated from image sequences using structure-from-motion techniques. Currently there is no general method for reconstruction of 3D models of arbitrary scenes from sparse data. We introduce an algorithm for recursive integration of sparse 3D structure to obtain a consistent model. This algorithm is shown to converge to the real scene structure as the number of views increases and to have a computational cost which is linear in the number of views. Results are presented for real and synthetic image sequences which demonstrate correct reconstruction for scenes containing significant occlusions.

The interpretation of the 3D world from image sequences requires the identification and correspondences of key features in the scene. We describe a robust algorithm for matching groupings of features related to the objects in the scene. We consider the propagation of uncertainty from the feature detection stage through the grouping stage to provide a measure of uncertainty at the matching stage. We focus upon indoor scenes and match junctions, which are groupings of line segments that meet at a single point. A model of the uncertainty in junction detection is described, and the junction uncertainty under the epipolar constraint is determined. Junction correspondence is achieved through matching of each line segment associated with the junction. A match likelihood is then derived based upon the detection uncertainties and then combined with information on junction topology to create a similarity measure. A robust matching algorithm is proposed and used to match junctions between pairs of images. The presented experimental results on real images show that the matching algorithm produces sufficiently reliable results for applications such as structure from motion.

This paper presents a new method to extract the 3D shape of objects from 3D gray level images using a bicubic B-spline surface model. Extraction of object shape is achieved through a hierarchical surface fitting by exploiting the multi-scale representation of the model. A strategy for converting the surface estimation into curve estimations is devised. The model surface is estimated by successively computing a set of cubic B-spline curves consisting of a coordinate curve net defining the surface. A regularising component is incorporated into the curve estimation to encourage the generation of an orthogonal coordinate curve net, preventing the creation of unwanted creases. Experimental results are presented for extracting the 3D shape of objects from real 3D images. 1 Introduction The interpretation of 3D images often needs the shape information of objects in the image. A set of unmodelled 3D structures derived from local low level operations (e.g. edge detection) [10] can hardly ever ...

This paper presents a method for extracting the 3D shape of objects from 3D gray level images. The object surface is assumed to be described by a bicubic B-spline surface. The detection of the object shape is achieved using a hierarchical approach in which the object surface is successively estimated using the spline model at increased scales. At each scale, the model is iteratively fitted to a set of measurement points which are believed to be on the object surface. The measurement points are detected by searching for voxels with the strongest edge response along the direction normal to the model surface. Searching for the measurement points is controlled within a search radius determined based on the edge strength at the points on the current model surface. The result is a parametric surface approximating the object surface to be detected.

This paper presents an unsupervised segmentation method applicable to both 2D and 3D images. The segmentation is achieved by a bottom-up hierarchical analysis to progressively agglomerate pixels/voxels in the image into non-overlapped homogeneous regions characterised by a linear signal model. A hierarchy of adjacency graphs is used to describe agglomeration results from the hierarchical analysis, and is constructed by successively performing a clustering operation which produces an optimal classification by merging each region with its nearest neighbours determined under the framework of statistical inference. The top level of the hierarchy then describes the segmentation result.

This paper describes a method for recovering the 3D shape of a moving object from a sequence of images. While following the motion of the object, a 3D surface model, initialized to be spherical, progressively deforms under the action of simulated external forces driving its profile towards the object profile extracted from the image. Internal forces coupled to the model encourage the surface to be smooth and to deform smoothly at each time step. The model is aligned with the object in 3D by rotating on the ground plane to orientate parallel to the back-projected motion trajectory and translating to minimize the distance between the model profile and the object profile. Experimental results are presented for the recovery of shape models of vehicles