Representation of local geometry in the visual system
Biological Cybernetics
Generic Neighborhood Operators
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Scale-Space: Its Natural Operators and Differential Invariants
IPMI '91 Proceedings of the 12th International Conference on Information Processing in Medical Imaging
Receptive field assembly pattern specificity
Journal of Visual Communication and Image Representation
Hypotheses for Image Features, Icons and Textons
International Journal of Computer Vision
The Second Order Local-Image-Structure Solid
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mode estimation using pessimistic scale space tracking
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Image features and the 1-D, 2nd
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
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We present theoretical and computational results that develop Koenderink's theory of feature analysis in human vision [1,7]. Employing a scale space framework, the method aims to classify image points into one of a limited number of feature categories on the basis of local derivative measurements up to some order. At the heart of the method is the use of a family of functions, members of which can be used to account for any set of image measurements. We will show how certain families of simple functions naturally induce a categorical structure onto the space of possible measurements. We present two such families suitable for 1D images measured up to 2nd order, and various results relevant to similar analysis of 2D images.