C. Rothwell, A. Zisserman, D. Forsyth, and J. Mundy. Fast recognition using algebraic invariants. In Geometric Invariance in Computer Vision, pages 398--407. MIT Press, 1992.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....systems using such data are very numerous. They differ in the kind of information they extract from the images, and in the dimension of the model (2D or 3D) Connell and Brady [CB87] use intensity data, Arbogast [AM91] uses occlusion contours, Mohr et al. MVQ93] use points, Rothwell et al.[RZFM92] use numerical invariants associated with some configura1 tions of points, lines and curves, Weiss uses differential invariants associated with algebraic curves [Wei92] Our approach falls in the last category. It relies on the idea that aspect graphs should be learned from examples rather than ....

C.A. Rothwell, A. Zisserman, D.A. Forsyth, and J.L. Mundy. Fast recognition using algebraic invariants. In J.L. Mundy and A. Zisserman, editors, Geometric Invariance in Computer Vision, chapter 20, pages 398--407. MIT Press, 1992.

....systems using such data are very numerous. They differ in the kind of information they extract from the images, and in the dimension of the model (2D or 3D) Connell and Brady [CB87] use intensity data, Arbogast [AM91] use occlusion contours, Mohr et al. MVQ93] use points, Rothwell et al.[RZFM92] use numerical invariants associated with some configurations of points, lines and curves, Weiss uses differential invariants associated with algebraic curves [Wei92] Our approach falls into this last category. The input consists of a large set of images. These images represent the object to be ....

C.A. Rothwell, A. Zisserman, D.A. Forsyth, and J.L. Mundy. Fast recognition using algebraic invariants. In J.L. Mundy and A. Zisserman, editors, Geometric Invariance in Computer Vision, chapter 20, pages 398--407. MIT Press, 1992.

....adjacent region list. Figure 8: Coloured region segmentation achieved by the algorithm for the image in figure 1. 3.2 Shape measures Describing general shape remains a challenging and as yet unsolved problem in computer vision. Polygonal planar regions may be described by projective invariants [11]. These numbers have the advantage that they remain unchanged as the viewpoint from which the shape is seen changes. They do not however map readily onto a persons perception of shape. The goal for a family of shape descriptors (for use in image retrieval) must be to allow a person to describe the ....

C.A. Rothwell, A. Zisserman, D.A. Forsyth, and J.L. Mundy. Fast Recognition using Algebraic Invariants, volume Geometric Invariance in Computer Vision. Mundy, J.L. and Zisserman, A., editors, MIT Press, 1992.

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C. Rothwell, A. Zisserman, D. Forsyth, and J. Mundy. Fast recognition using algebraic invariants. In Geometric Invariance in Computer Vision, pages 398--407. MIT Press, 1992.

....applications require no camera calibration or pose information, and models are generated and verified directly from images. 1 Introduction There has been considerable recent success in using projective invariants of plane algebraic curves as index functions for recognition in model based vision [10, 15, 23, 28]. Less attention has been given to invariants for smooth non algebraic curves. In this paper we present a novel and simple method of constructing a family of invariants for non convex smooth curves. Lamdan et al. 18] proposed and implemented a canonical frame construction. We improve on this in ....

....case that the transformation might actually be affine, because the affine group is a sub group of the projective group. 2. Recognition is entirely via index functions based on projective invariants. In [18] recognition was a mixture of indexing and Hough style voting. As has been argued elsewhere [10, 23]. there is considerable benefit in using invariants to imaging transformations as indexing functions for generating recognition hypotheses. In particular, such functions only involve image measurements and avoid comparison CAR acknowledges the support of GE. AZ acknowledges the support of the ....

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Rothwell, C.A., Zisserman, A. Forsyth, D.A. and Mundy, J.L. "Fast Recognition using Algebraic Invariants," Geometric Invariance in Computer Vision, Mundy, J.L. and Zisserman, A., editors, MIT Press, 1992.

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Rothwell, C.A., Zisserman, A.P, Forsyth, D.A. and Mundy, J.L., "Fast recognition using algebraic invariants," in J.L. Mundy and A.P. Zisserman (ed.s) Geometric Invariance in Computer Vision, MIT Press, 1992.

....usually constructed using the techniques of invariant theory. As a result, these functions have the same value for any view of a given object, and so can be used to index into a model base without search. Indexing functions and systems that use indexing, are extensively described in [10, 11] and [16] displays the general architecture used in such systems. To date, indexing functions have been demonstrated only for plane and polyhedral objects. Constructing indexing functions for curved surfaces is more challenging, because the indexing function must compute a description of the surface s ....

Rothwell, C.A., Zisserman, A.P., Forsyth, D.A. and Mundy, J.L., "Fast Recognition using Algebraic Invariants," in J.L. Mundy and A.P. Zisserman (ed.s) Geometric Invariance in Computer Vision, MIT Press, 1992.

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