Results 1  10
of
22
Spatstat: An R package for analyzing spatial point patterns
 Journal of Statistical Software
, 2005
"... spatstat is a package for analyzing spatial point pattern data. Its functionality includes exploratory data analysis, modelfitting, and simulation. It is designed to handle realistic datasets, including inhomogeneous point patterns, spatial sampling regions of arbitrary shape, extra covariate data, ..."
Abstract

Cited by 62 (2 self)
 Add to MetaCart
spatstat is a package for analyzing spatial point pattern data. Its functionality includes exploratory data analysis, modelfitting, and simulation. It is designed to handle realistic datasets, including inhomogeneous point patterns, spatial sampling regions of arbitrary shape, extra covariate data, and ‘marks ’ attached to the points of the point pattern. A unique feature of spatstat is its generic algorithm for fitting point process models to point pattern data. The interface to this algorithm is a function ppm that is strongly analogous to lm and glm. This paper is a general description of spatstat and an introduction for new users.
Fingerprint Features  Statistical Analysis and System Performance Estimates
, 1999
"... As the need for personal authentication increases, many people are turning to biometric authentication as an alternative to traditional security devices. Concurrently, users and vendors of biometric authentication systems are searching for methods to establish system performance. This paper presen ..."
Abstract

Cited by 19 (0 self)
 Add to MetaCart
As the need for personal authentication increases, many people are turning to biometric authentication as an alternative to traditional security devices. Concurrently, users and vendors of biometric authentication systems are searching for methods to establish system performance. This paper presents a model that defines t...
SelfCustomized BSP Trees for Collision Detection
, 2000
"... The ability to perform efficient collision detection is essential in virtual reality environments and their applications, such as walkthroughs. In this paper we reexplore a classical structure used for collision detection  the binary space partitioning tree. Unlike the common approach, which a ..."
Abstract

Cited by 16 (1 self)
 Add to MetaCart
The ability to perform efficient collision detection is essential in virtual reality environments and their applications, such as walkthroughs. In this paper we reexplore a classical structure used for collision detection  the binary space partitioning tree. Unlike the common approach, which attributes equal likelihood to each possible query, we assume events that happened in the past are more likely to happen again in the future. This leads us to the definition of selfcustomized data structures. We report encouraging results obtained while experimenting with this concept in the context of selfcustomized bsp trees. Keywords: Collision detection, binary space partitioning, selfcustomization. 1 Introduction Virtual reality refers to the use of computer graphics to simulate physical worlds or to generate synthetic ones, where a user is to feel immersed in the environment to the extent that the user feels as if "objects" seen are really there. For example, "objects" should m...
Modelling spatial point patterns in R
 Case Studies in Spatial Point Pattern Modelling. Lecture Notes in Statistics 185, 23–74
, 2006
"... Summary. We describe practical techniques for fitting stochastic models to spatial point pattern data in the statistical package R. The techniques have been implemented in our package spatstat in R. They are demonstrated on two example datasets. 1 ..."
Abstract

Cited by 13 (3 self)
 Add to MetaCart
(Show Context)
Summary. We describe practical techniques for fitting stochastic models to spatial point pattern data in the statistical package R. The techniques have been implemented in our package spatstat in R. They are demonstrated on two example datasets. 1
Indices of Dependence Between Types in Multivariate Point Patterns
 Scandinavian Journal of Statistics
, 1999
"... We propose new summary statistics quantifying several forms of dependence between types in a spatial pattern of points classified into distinct types. These statistics are the multivariate counterparts of the Jfunction for point processes of a single type, introduced in [18]. They are based on comp ..."
Abstract

Cited by 11 (3 self)
 Add to MetaCart
(Show Context)
We propose new summary statistics quantifying several forms of dependence between types in a spatial pattern of points classified into distinct types. These statistics are the multivariate counterparts of the Jfunction for point processes of a single type, introduced in [18]. They are based on comparing distances from a type i point to either the nearest type j point or to the nearest point in the pattern regardless of type to these distances seen from an arbitrary point in space. Information about the range of interaction can also be inferred. Our statistics can be computed explicitly for a range of wellknown multivariate point process models. Some applications to bivariate data sets are presented as well. Keywords & Phrases: ants' nests, beta cells, empty space function, hamster tumour, J  function, multitype point patterns, myrtle disease, nearestneighbour distance distribution function, point processes, random labelling, spatial interaction, spatial statistics. AMS Mathemat...
Ant colony optimization for image regularization based on a nonstationary Markov modeling
 IEEE Trans. on Image Processing, Mar. 2007
"... Abstract—Ant colony optimization (ACO) has been proposed as a promising tool for regularization in image classification. The algorithm is applied here in a different way than the classical transposition of the graph color affectation problem. The ants collect information through the image, from on ..."
Abstract

Cited by 6 (0 self)
 Add to MetaCart
(Show Context)
Abstract—Ant colony optimization (ACO) has been proposed as a promising tool for regularization in image classification. The algorithm is applied here in a different way than the classical transposition of the graph color affectation problem. The ants collect information through the image, from one pixel to the others. The choice of the path is a function of the pixel label, favoring paths within the same image segment. We show that this corresponds to an automatic adaptation of the neighborhood to the segment form, and that it outperforms the fixedform neighborhood used in classical Markov random field regularization techniques. The performance of this new approach is illustrated on a simulated image and on actual remote sensing images.
Computing Morphological Properties of Arrangements of Lines
, 1991
"... An arrangement of n lines in the plane is a partition of the plane into O(n²) faces, edges, and vertices (intersection points). Such line processes play a fundamental role in modeling spatial patterns and studying a variety of problems such as traffic flow. We briefly survey recent results on the ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
An arrangement of n lines in the plane is a partition of the plane into O(n²) faces, edges, and vertices (intersection points). Such line processes play a fundamental role in modeling spatial patterns and studying a variety of problems such as traffic flow. We briefly survey recent results on the complexity of computing morphological properties of such arrangements.
Accurate MultiDimensional . . .
"... We present an accurate and efficient method to generate samples based on a Poissondisk distribution. This type of distribution, because of its blue noise spectral properties, is useful for image sampling. It is also useful for multidimensional Monte Carlo integration and as part of a procedural ob ..."
Abstract
 Add to MetaCart
We present an accurate and efficient method to generate samples based on a Poissondisk distribution. This type of distribution, because of its blue noise spectral properties, is useful for image sampling. It is also useful for multidimensional Monte Carlo integration and as part of a procedural object placement function. Our method extends trivially from 2D to 3D or to any higher dimensional space. We demonstrate results for up to four dimensions, which are likely to be the most useful for Computer Graphics applications. The method is accurate because it generates distributions with the same statistical properties of those generated with the brute force dartthrowing algorithm, the archetype against which all other Poissondisk sampling methods are compared. The method is efficient because it employs a spatial subdivision data structure that signals the regions of space where the insertion of new samples is allowed. The method has O(N log N) time and space complexity relative to the total number of samples. The method generates maximal distributions in which no further samples can be inserted at the completion of the algorithm. The method is only limited in the number of samples it can generate and the number of dimensions over which it can work by the available physical memory.