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This paper addresses the problem of the local scale parameter selection for recognition techniques based on Gaussian derivatives. Patterns are described in a feature space of which each dimension is a scale and orientation normalized receptive field (a unit composed of normalized Gaussian-based filters). Scale invariance is obtained by automatic selection of an appropriate local scale [Lin98b] and followed by normalisation of the receptive field to the appropriate scale. Orientation invariance is obtained by the determination of the dominant local orientation and by steering the receptive fields to this orientation. Data is represented structurally in a feature space that is designed for the recognition of static object configurations. In this space an image is modeled by the vectorial representation of the receptive field responses at each pixel, forming a surface in the feature space. Recognition is achieved by measuring the distance between the vector of normalized receptive fields responses of an observed neighborhood and the surface point of the image model. The power of a scale equivariant feature space is validated by experimental results for point correspondences in images of different scales and the recognition of objects under different view points.

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