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We describe a model based recognition system, called LEWIS, for the identiication of planar objects based on a projectively invariant representation of shape. The advantages of this shape description include simple model acquisition (direct from images), no need for camera calibration or object pose computation, and the use of index functions. We describe(More)
Recently, vision research has centred on the extraction and organization of geometric features, and on geometric relations. It is largely assumed that topological structure, that is linked edgel chains and junctions, cannot be extracted reliably from image intensity data. In this paper we demonstrate that this view is overly pessimistic and that visual(More)
We present a canonical frame construction for determining pro-jectively invariant indexing functions for non-algebraic smooth plane curves. These invariants are semi-local rather than global, which promotes tolerance to occlusion. Two applications are demonstrated. Firstly, we report preliminary work on building a model based recognition system for planar(More)
Shape from symmetry-detecting and exploiting symmetry in aane images. 24] R. J. Woodham. Photometric stereo: A reeectance map technique for determining surface orientation from image intensity. 10] H. Hayakawa. Photometric stereo under a light-source with arbitrary motion. J. 14] D.R. Hougen and N. Ahuja. Estimation of the light source distribution and its(More)
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. In this paper we consider projective reconstructions from pairs of(More)
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 copla-nar curves, given such a frame independent representation. This result is(More)