Please note: In order to keep Hive up to date and provide users with the best features, we are no longer able to fully support Internet Explorer. The site is still available to you, however some sections of the site may appear broken. We would encourage you to move to a more modern browser like Firefox, Edge or Chrome in order to experience the site fully.

A Taxonomy for Texture Description and Identification, PDF eBook

A Taxonomy for Texture Description and Identification PDF

Part of the Springer Series in Perception Engineering series

PDF

Please note: eBooks can only be purchased with a UK issued credit card and all our eBooks (ePub and PDF) are DRM protected.

Description

A central issue in computer vision is the problem of signal to symbol transformation.

In the case of texture, which is an important visual cue, this problem has hitherto received very little attention.

This book presents a solution to the signal to symbol transformation problem for texture.

The symbolic de- scription scheme consists of a novel taxonomy for textures, and is based on appropriate mathematical models for different kinds of texture.

The taxonomy classifies textures into the broad classes of disordered, strongly ordered, weakly ordered and compositional.

Disordered textures are described by statistical mea- sures, strongly ordered textures by the placement of primitives, and weakly ordered textures by an orientation field.

Compositional textures are created from these three classes of texture by using certain rules of composition.

The unifying theme of this book is to provide standardized symbolic descriptions that serve as a descriptive vocabulary for textures.

The algorithms developed in the book have been applied to a wide variety of textured images arising in semiconductor wafer inspection, flow visualization and lumber processing.

The taxonomy for texture can serve as a scheme for the identification and description of surface flaws and defects occurring in a wide range of practical applications.

Information

Information

Also in the Springer Series in Perception Engineering series  |  View all