The paper presents an algorithmbased on scale-space analysis, for the recognition of blurred planar objects. Apart from satisfying the usual requirements for invariance under translation, rotation and scaling, the al...
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The paper presents an algorithmbased on scale-space analysis, for the recognition of blurred planar objects. Apart from satisfying the usual requirements for invariance under translation, rotation and scaling, the algorithm is also invariant under blurring, that is, across all levels of detail or scales. The technique makes use of the spatial coincidence of the infiexion points on the object contour at all scales, and of the fact that no new such points are created as the object becomes more blurred. The algorithm therefore searches for the best match of these points at a single scale in the scale-space image. The algorithm was implemented on an IBM/AT in Modula-2 programming language, and was tested out on a group of 20 geographical maps of different sizes and at varying distances from the camera. A recognition rate of 95 to 100% and an average recognition time of 2.5 seconds were obtained by an efficient organisation of the template dictionary.
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