In this paper, a median filtering-based hierarchical motion vector estimation scheme making use of a pyramidal data structure is proposed. Compared to the conventional hierarchical motion vector estimation schemes, the proposed scheme overcomes the problem of propagation of false motion vectors across resolutions. Simulation studies show that the proposed scheme not only improves the prediction...

We introduce a nonlinear refinement subdivision scheme based on median-interpolation. The scheme constructs a polynomial interpolating adjacent block medians of an underlying object. The interpolating polynomial is then used to impute block medians at the next finer triadic scale. Perhaps surprisingly, expressions for the refinement operator can be obtained in closed-form for the scheme interpo...

In response to the flaw that the median filtering algorithm has a poor handling capacity to high-density and fine texture noise, a Dynamic window-based adaptive median filter algorithm is proposed. According to the associated level between noise-point information and the surrounding, the new algorithm adjust, the Noise point filter value, which can get a better deal with the details of the imag...

This paper presents sorting network based architectures for computing non-recursive and recursive median lters. The proposed architectures are highly pipelined, and consist of fewer compare-swap units than existing architectures. The reduction in the number of compare-swap units is achieved by minimizing computational overlap between successive outputs, and also by using Batcher's odd-even merg...

In supervised machine learning, some algorithms are restricted to discrete data and thus need to discretize continuous attributes. In this paper, we present a new discretization method called MODL, based on a Bayesian approach. The MODL method relies on a model space of discretizations and on a prior distribution defined on this model space. This allows the setting up of an evaluation criterion...

We study uniform bounds associated with the Allen–Cahn equation and its numerical discretization schemes. These uniform bounds are different from, and weaker than, the conventional energy dissipation and the maximum principle, but they can be helpful in the analysis of numerical methods. In particular, we show that finite difference spatial discretization, like the original continuum model, sha...

This paper argues that two commonly-used discretization approaches, fixed k-interval discretization and entropy-based discretization have sub-optimal characteristics for naive-Bayes classification. This analysis leads to a new discretization method, Proportional k-Interval Discretization (PKID), which adjusts the number and size of discretized intervals to the number of training instances, thus...

We compare four discretization methods, all based on entropy: the original C4.5 approach to discretization, two globalized methods, known as equal interval width and equal frequency per interval, and a relatively new method for discretization called multiple scanning using the C4.5 decision tree generation system. The main objective of our research is to compare the quality of these four method...

This report discusses how soft discretization can be implemented to train a discrete Bayesian Network directly from continuous data. The method consists of a soft discretization step that converts the continuous variables of the training cases into soft evidence, followed by a suitable parameter learning algorithm for the Bayesian Network. The learning algorithm is a modification of the Maximum...