1,391 research outputs found
Expressing the entropy of lattice systems as sums of conditional entropies
Whether a system is to be considered complex or not depends on how one
searches for correlations. We propose a general scheme for calculation of
entropies in lattice systems that has high flexibility in how correlations are
successively taken into account. Compared to the traditional approach for
estimating the entropy density, in which successive approximations builds on
step-wise extensions of blocks of symbols, we show that one can take larger
steps when collecting the statistics necessary to calculate the entropy density
of the system. In one dimension this means that, instead of a single sweep over
the system in which states are read sequentially, one take several sweeps with
larger steps so that eventually the whole lattice is covered. This means that
the information in correlations is captured in a different way, and in some
situations this will lead to a considerably much faster convergence of the
entropy density estimate as a function of the size of the configurations used
in the estimate. The formalism is exemplified with both an example of a free
energy minimisation scheme for the two-dimensional Ising model, and an example
of increasingly complex spatial correlations generated by the time evolution of
elementary cellular automaton rule 60
Approximation of the Euclidean distance by Chamfer distances
Chamfer distances play an important role in the theory of distance transforms. Though the determination of the exact Euclidean distance transform is also a well investigated area, the classical chamfering method based upon "small" neighborhoods still outperforms it e.g. in terms of computation time. In this paper we determine the best possible maximum relative error of chamfer distances under various boundary conditions. In each case some best approximating sequences are explicitly given. Further, because of possible practical interest, we give all best approximating sequences in case of small (i.e. 5x5 and 7x7) neighborhoods
Hexagonal structure for intelligent vision
Using hexagonal grids to represent digital images have been studied for more than 40 years. Increased processing capabilities of graphic devices and recent improvements in CCD technology have made hexagonal sampling attractive for practical applications and brought new interests on this topic. The hexagonal structure is considered to be preferable to the rectangular structure due to its higher sampling efficiency, consistent connectivity and higher angular resolution and is even proved to be superior to square structure in many applications. Since there is no mature hardware for hexagonal-based image capture and display, square to hexagonal image conversion has to be done before hexagonal-based image processing. Although hexagonal image representation and storage has not yet come to a standard, experiments based on existing hexagonal coordinate systems have never ceased. In this paper, we firstly introduced general reasons that hexagonally sampled images are chosen for research. Then, typical hexagonal coordinates and addressing schemes, as well as hexagonal based image processing and applications, are fully reviewed. © 2005 IEEE
- …