Linear Scale - Space Theory from Physical

In the past decades linear scale-space theory was derived on the basis of various axiomatics. In this paper we revisit these axioms and show that they merely coincide with the following physical principles, namely that the image domain is a Galilean space, that the total energy exchange between a region and its surrounding is preserved under linear ltering and that the physical observables should be invariant under the group of similarity transformations. These observables are elements of the similarity jet spanned by natural coordinates and diierential energies read out by a vision system. Furthermore, linear scale-space theory is extended to spatio-temporal images on bounded and curved domains. Our theory permits a delay-operation at the present moment which is in agreement with the motion detection model of Reichardt. In this respect our theory deviates from that of 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 diierence between our stochastic approach and that of Lindeberg is pointed out. The connection between continuous and (semi-)discrete sale-space theory for innnitely high scales observed by Lindeberg is reened 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 and odd coordinates sum can be guaranteed simultaneously by taking a normalised linear combination of the identity operator and the rst step discrete Green's functions. The new discrete Green's functions are still intimately related to the continuous Green's functions and appear to coincide with pyramidal discrete Green's functions.