Recently, different approaches for uncalibrated stereo have been suggested which permit projective reconstruction from multiple views. These use weak calibration which is represented by the epipolar geometry, and so no knowledge of the intrinsic or extrinsic camera parameters is required. We consider projective reconstructions from pairs of views, and compare a number of the available methods. Consequently we conclude which methods are most likely to be of use in applications that are dependent on 3D uncalibrated reconstructions.>

In any object recognition system a major and primary task is to associate those image features, within an image of a complex scene, that arise from an individual object. The key idea here is that a geometric class defined in 3D induces relationships in the image which must hold between points on the image outline (the perspective projection of the object). The resulting image constraints enable both identification and grouping of image features belonging to objects of that class. The classes include surfaces of revolution, canal surfaces (pipes) and polyhedra. Recognition proceeds by first recognising an object as belonging to one of the classes (for example a surface of revolution) and subsequently identifying the object (for example as a particular vase). This differs from conventional object recognition systems where recognition is generally targetted at particular objects. These classes also support the computation of 3D invariant descriptions including symmetry axes, canonical coordinate frames and projective signatures. The constraints and grouping methods are viewpoint invariant, and proceed with no information on object pose. We demonstrate the effectiveness of this class-based grouping on real, cluttered scenes using grouping algorithms developed for rotationally symmetric surfaces, canal-surfaces and polyhedra.>

A recognition strategy consisting of a mixture of indexing on invariants and search, allows objects to be recognised up to a Euclidean ambiguity with an uncalibrated camera. The approach works by using projective invariants to determine all the possible projectively equivalent models for a particular imaged object; then a system of global consistency constraints is used to determine which of these projectively equivalent, but Euclidean distinct, models corresponds to the objects viewed. These constraints follow from properties of the imaging geometry. In particular, a recognition hypothesis is equivalent to an assertion about, among other things, viewing conditions and geometric relationships between objects, and these assertions must be consistent for hypotheses to be correct. The approach is demonstrated to work on images of real scenes consisting of polygonal objects and polyhedra.>

Over the last several years the concept of model-supported exploitation (MSE) has evolved to a point where relatively simple computer vision algorithms can extract significant intelligence information from aerial images in a robust and reliable manner. Information extraction is enabled by the use of detailed 3D site models which provide an extensive context for the application of image analysis algorithms. This paper reviews the basic MSE concept and illustrates the approach using three operational concepts taken from the RADIUS project, quick-look, detection and counting and focussed change detection.>

It is known that rotationally symmetric surfaces can be recognized from their outlines alone, using cross-ratios of bitangent intersections. A successful implementation of this technique is demonstrated using a novel bitangent finder which works on images of real scenes. The stability of the cross-ratios is reported and compared to affine invariants. The recognition technique is shown to extend to the case of straight homogeneous generalized cylinders.>

A number of recent papers have argued that invariants do not exist for three-dimensional point sets in general position, which has often been misinterpreted to mean that invariants cannot be computed for any three-dimensional structure. It is proved by example that although the general statement is true, invariants do exist for structured three-dimensional point sets. Projective invariants are derived for two object classes: the first is for points that lie on the vertices of polyhedra, and the second for objects that are projectively equivalent to ones possessing a bilateral symmetry. The motivations for computing such invariants are twofold: they can be used for recognition, and they can be used to compute projective structure. Examples of invariants computed from real images are given.>

A family of qualitative methods is described that determines structure of curves in a scene from their image projections in two or more views. For example, plane curves are distinguished from space curves, and space curves from contour generators. The novelty of the approach is that first, it is unaffected by camera intrinsic parameters, so calibration is not required; second, it is also unaffected by camera motion, so viewer motion need not be known; and third, curves need not be matched point by point. Qualitative scene descriptions can be determined using relative measurements, and affine or projective properties alone. The practical power of this family of methods is demonstrated using a real-time vision system based on B-spline snake-tracking.>

Projectively invariant shape descriptors allow fast indexing into model libraries without the need for pose computation or camera calibration. Progress in building a model-based vision system for plane objects that uses algebraic projective invariants is described. A brief account of these descriptors is given, and the recognition system is described, giving examples of the invariant techniques working on real images.>

Invariant descriptors are shape descriptors that are unaffected by object pose, by perspective projection, or by the intrinsic parameters of the camera. These descriptors can be constructed using the methods of invariant theory, which are briefly surveyed. A range of applications of invariant descriptors in 3D model-based vision is demonstrated. First, a model-based vision system that recognizes curved plane objects irrespective of their pose is demonstrated. Curves are not reduced to polyhedral approximations but are handled as objects in their own right. Models are generated directly from image data. Once objects have been recognized, their pose can be computed. Invariant descriptors for 3D objects with plane faces are described. All these ideas are demonstrated using images of real scenes. The stability of a range of invariant descriptors to measurement error is treated in detail.>

Projectively invariant shape descriptors efficiently identify instances of object models in images without reference to object pose. These descriptions rely on frame independent representations of planar curves, using plane conies. We show that object pose can be determined from coplanar curves, given such a frame independent representation. This result is demonstrated for real image data. The shape of objects in images changes as the camera is moved around. This extremely simple observation represents the dominant problem in model based vision. Nielsen [4, 5] first suggested using projectively invariant labels as landmarks for navigation. Recent papers [1, 2] have shown that it is possible to compute shape descriptors of arbitrary plane objects that are unaffected by