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Mathematical statistics --- Spatial analysis (Statistics) --- 519.218 --- Analysis, Spatial (Statistics) --- Correlation (Statistics) --- Spatial systems --- Special stochastic processes --- MET Methods & Techniques --- binary images --- methods & techniques --- pattern --- spatial processes --- statistics --- 519.218 Special stochastic processes

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Presents the first comprehensive guide to the analysis of spatial data. Each chapter covers a particular data format and the associated class of problems, introducing theory, giving computational suggestions, and providing examples. Methods are illustrated by computer-drawn figures. Serves as an introduction to this rapidly growing research area for mathematicians and statisticians, and as a reference to new computer methods for research workers in ecology, geology, archeology, and the earth sciences.

Mathematical statistics --- Spatial analysis (Statistics) --- Analyse spatiale (Statistique) --- 519.24 --- Analysis, Spatial (Statistics) --- Correlation (Statistics) --- Spatial systems --- Special statistical applications and models --- Spatial analysis (Statistics). --- 519.24 Special statistical applications and models

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Neural networks (Computer science) --- Pattern recognition systems --- 681.3*I5 --- 681.3*I5 Pattern recognition (Computing methodologies) --- Pattern recognition (Computing methodologies) --- Pattern classification systems --- Pattern recognition computers --- Pattern perception --- Computer vision --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Artificial intelligence --- Natural computation --- Soft computing --- Artificial intelligence. Robotics. Simulation. Graphics --- Mathematical statistics --- Pattern recognition systems. --- Neural networks (Computer science). --- Réseaux neuronaux (Informatique) --- Reconnaissance des formes (Informatique)

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The study of spatial processes and their applications is an important topic in statistics and finds wide application particularly in computer vision and image processing. This book is devoted to statistical inference in spatial statistics and is intended for specialists needing an introduction to the subject and to its applications. One of the themes of the book is the demonstration of how these techniques give new insights into classical procedures (including new examples in likelihood theory) and newer statistical paradigms such as Monte-Carlo inference and pseudo-likelihood. Professor Ripley also stresses the importance of edge effects and of lack of a unique asymptotic setting in spatial problems. Throughout, the author discusses the foundational issues posed and the difficulties, both computational and philosophical, which arise. The final chapters consider image restoration and segmentation methods and the averaging and summarising of images. Thus, the book will find wide appeal to researchers in computer vision, image processing, and those applying microscopy in biology, geology and materials science, as well as to statisticians interested in the foundations of their discipline.

Spatial analysis (Statistics)