Scaling Theorems for Zero Crossings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Uniqueness of the Gaussian Kernel for Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scaling Theorems for Zero Crossings of Bandlimited Signals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space Properties of Quadratic Feature Detectors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Local Weighted Averaging Methods in Contour Smoothing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space Derived From B-Splines
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Deriving and Mining Spatiotemporal Event Schemas in In-Situ Sensor Data
ICCSA '08 Proceeding sof the international conference on Computational Science and Its Applications, Part I
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Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Connecting the dots: constructing spatiotemporal episodes from events schemas
Transactions on Computational Science VI
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Two scaling theorems are given. Instead of delta functions, polynomial functions are used as input. The advantages are twofold: the smoothness conditions on kernels are not required, so that kernels of the form e/sup -k mod X mod / can be included; the proofs are based on calculus completely and so can be more easily understood.