Sample Elimination for Generating Poisson Disk Sample Sets

Sample Elimination for Generating Poisson Disk Sample Sets

In this paper we describe sample elimination for generating Poisson disk sample sets with a desired size. We introduce a greedy sample elimination algorithm that assigns a weight to each sample in a given set and eliminates the ones with greater weights in order to pick a subset of a desired size with Poisson disk property without having to specify a Poisson disk radius. This new algorithm is s...

Feature Selection for Small Sample Sets with High Dimensional Data Using Heuristic Hybrid Approach

Feature selection can significantly be decisive when analyzing high dimensional data, especially with a small number of samples. Feature extraction methods do not have decent performance in these conditions. With small sample sets and high dimensional data, exploring a large search space and learning from insufficient samples becomes extremely hard. As a result, neural networks and clustering a...

'Leprosy elimination'--need for sample survey.

A Comparison of Methods for Generating Poisson Disk Distributions

Abstract Poisson disk distributions have many applications in the field of computer graphics. Besides sampling, Poisson disk distributions are used in object distribution, non-photorealistic rendering and procedural texturing. Over the years, a large number of methods for generating Poisson disk distributions have been proposed, making it difficult to choose the right method for a given applica...

Sample Sizes to Compare Two Poisson Rates

In this note, procedures for determining the sample sizes needed to compare the rates of two Poisson populations to achieve a pre-specifi ed power at a given ratio of the rates are proposed. The fi rst method relies on a conditional uniformly most powerful test (CUMPT) which leads to sample sizes that will guarantee the desired power, but at the cost of using more units than necessary. The seco...