M arkovian approaches to early vision processes need a huge amount of computing power. These algorithms can usually be implemented on parallel computing structures. Herein, we show that the Markovian labeling approach can be implemented in fully parallel cellular network architectures, using simple functions and data representations. This makes possible to implement our model in parallel imagin...

The current work applies Conditional Random Fields to the problem of temporal reference mapping from Chinese text to English text. The learning algorithm utilizes a moderate number of linguistic features that are easy and inexpensive to obtain. We train a tense classifier upon a small amount of manually labeled data. The evaluation results are promising according to standard measures as well as...

ABSTRACT Producing thousands of simulations the dark matter distribution in Universe with increasing precision is a challenging but critical task to facilitate exploitation current and forthcoming cosmological surveys. Many inexpensive substitutes full N-body have been proposed, even though they often fail reproduce statistics smaller non-linear scales. Among these alternatives, common approxim...

Compact analytic expressions are derived for the mean, mutual intensity, and spatial covariance of the acoustic field forward propagated though a stratified ocean waveguide containing three-dimensional random surface and volume inhomogeneities. The inhomogeneities need not obey a stationary random process in space, can be of arbitrary composition and size relative to the wavelength, or can have...

For over half a century, psychologists have been struck by how poor people are at expressing their internal sensations, impressions, and evaluations via rating scales. When individuals make judgments, they are incapable of using an absolute rating scale, and instead rely on reference points from recent experience. This relativity of judgment limits the usefulness of responses provided by indivi...

The similarity jet of a linear scale-space is described in its most concise set of local and multi-local Euclidean invariants. The stability and (partial) equivalence of topologies on these invariants regardless additive uniform Gaussian noise is demonstrated.

Amorphological scale-space representation is presented based on a morphological strong filter, the levelings. The scale-properties are analysed and illustrated. From one scale to the next, details vanish, but the contours of the remaining objects are preserved sharp and perfectly localised. This paper is followed by a companion paper on pde formulations of levelings.

We define mutually consistent scale-space theories for scalar and vector images. Consistency pertains to the connection between the already established scalar theory and that for a suitably defined scalar field induced by the proposed vector scale-space. We show that one is compelled to reject the Gaussian scale-space paradigm in certain cases when scalar and vector fields are mutually dependen...