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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scaling Theorems for Zero-Crossings
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Skeletonization of fingerprint based-on modulus minima of wavelet transform
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
Connecting the dots: constructing spatiotemporal episodes from events schemas
Transactions on Computational Science VI
| Hi-index | 0.14 |
Scale-space filtering is the only known method which provides a hierarchic signal description method by extracting features across a continuum of scales. One of its important characteristics is that it demands the filtering involved does not create generic features as the scale increases. It has been shown in [4], [5], [6] that the Gaussian filter is unique in holding this remarkable property. This is in essence the so-called scaling theorem. In this paper, we propose two scaling theorems for band-limited signals. They are applicable to a broader class of signals and a bigger family of filtering kernels than in [4], [5],[6]. An in-depth discussion of our theorems and the previously published ones is also given.