Uniqueness of the Gaussian Kernel for Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representation of local geometry in the visual system
Biological Cybernetics
The scale-space formulation of pyramid data structures
Parallel computer vision
A Multiresolution Hierarchical Approach to Image Segmentation Based on Intensity Extrema
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Algorithms for Low-Level Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generic Neighborhood Operators
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric methods for analysis of ridges in n-dimensional images
Geometric methods for analysis of ridges in n-dimensional images
Differential and Integral Geometry of Linear Scale-Spaces
Journal of Mathematical Imaging and Vision
Topological Numbers and Singularities in Scalar Images: Scale-Space Evolution Properties
Journal of Mathematical Imaging and Vision
Geometry-Driven Diffusion in Computer Vision
Geometry-Driven Diffusion in Computer Vision
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
A Pyramid Framework for Early Vision: Multiresolutional Computer Vision
A Pyramid Framework for Early Vision: Multiresolutional Computer Vision
An Extended Class of Scale-Invariant and Recursive Scale Space Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space with Casual Time Direction
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Linear Spatio-Temporal Scale-Space
SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
On the Duality of Scalar and Density Flows
SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
Combining greyvalue invariants with local constraints for object recognition
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
A representation for visual information with application to machine vision
A representation for visual information with application to machine vision
Differential and Integral Geometry of Linear Scale-Spaces
Journal of Mathematical Imaging and Vision
Linearised Euclidean Shortening Flow of Curve Geometry
International Journal of Computer Vision
Linear Scale-Space has First been Proposed in Japan
Journal of Mathematical Imaging and Vision
A Dynamic Scale–Space Paradigm
Journal of Mathematical Imaging and Vision
International Journal of Computer Vision
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
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In the past decades linear scale-space theory was derivedon the basis of various axiomatics. In this paper we revisit these axioms and show that they merely coincide with the followingphysical principles, namely that the image domain is a Galileanspace, that the total energy exchange between a region and itssurrounding is preserved under linear filtering and that the physical observables should be invariant under the group of similarity transformations. These observables are elements of the similarity jet spanned bynatural coordinates and differential energies read out by a vision system.Furthermore, linear scale-space theory is extended to spatio-temporalimages on bounded and curved domains. Our theory permits a delay-operationat the present moment which is in agreement with the motion detection model of Reichardt. In this respect our theory deviates from thatof Koenderink which requires additional syntactical operators to realise such a delay-operation.Finally, the semi-discrete and discrete linear scale-space theories are derived by discretising the continuous theories following the theory of stochastic processes. The relation and difference between our stochastic approach and that of Lindeberg is pointed out. The connection between continuous and (semi-)discretesale-space theory for infinitely high scales observed by Lindeberg is refined by applying appropriate scaling limits. It is shown that Lindeberg‘s requirement of normalisation for one-dimensional discrete Green‘s functions can be incorporated into our theory for arbitrary dimensional discrete Green‘s functions, parameter determination can be avoided, and the requirement of operation at even andodd coordinates sum can be guaranteed simultaneously by taking a normalised linear combination of the identity operator and the first step discrete Green‘s functions. The new discrete Green‘s functions are still intimatelyrelated to the continuous Green‘s functions and appear to coincide withpyramidal discrete Green‘s functions.