Charlie Rothwell

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We describe a model based recognition system, called LEWIS, for the identification 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)
A number of recent papers have argued that invariants do not exist for three dimensional point sets in general position 3, 4, 13]. This has often been misinterpreted to mean that invariants cannot be computed for any three dimensional structure. This paper proves by example that although the general statement is true, in-variants do exist for structured(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)
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 deened in 3D induces relationships in the image which must hold between points on the image outline (the perspective projection of the object).(More)
The systems and concepts described in this paper document the evolution of the geometric invariance approach to object recognition over the last ve years. Invariance overcomes one of the fundamental di culties in recognising objects from images: that the appearance of an object depends on viewpoint. This problem is entirely avoided if the geometric(More)
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(More)
Projectively invariant shape descriptors allow fast indexing into model libraries without the need for pose computation or camera calibration. This paper describes progress in building a model based vision system for plane objects that uses algebraic projective invari-ants. We give a brief account of these descriptors and then describe the recognition(More)