Uniqueness of the Gaussian Kernel for Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space for Discrete Signals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scaling Theorems for Zero-Crossings
IEEE Transactions on Pattern Analysis and Machine Intelligence
A unified distance transform algorithm and architecture
Machine Vision and Applications
A Spatio-Frequency Trade-Off Scale for Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gaussian Scale-Space Theory
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
KCS-new kernel family with compact support in scale space: formulation and impact
IEEE Transactions on Image Processing
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This paper addresses the issue of a higher dimensional discrete scale-space (DSS) formulation. The continuous linear scale-space theory provides a unique framework for visual front-end processes. In practice, a higher dimensional DSS formulation is necessary since higher dimensional discrete signals must be dealt with. In this paper, first we examine the approximation fidelity of the commonly used sampled Gaussian. Second, we propose a generalized DSS formulation for 2-D and 3-D signals. The DSS theory has been presented at first by Lindeberg. While his 1-D DSS formulation is complete, the formulation as related to the extension to higher dimensions has not been fully derived. Furthermore, we investigate the properties of our derived DSS kernels and present the results of a validation study with respect to both smoothing and differentiation performance.