Approximation of Euclidean Metric by Digital Distances / by Jayanta Mukhopadhyay.

Format:

Book

Author/Creator:

Mukhopādhyāẏa, Jaẏanta, Author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Language:

English

Subjects (All):

Computer vision.

Pattern recognition systems.

Functional analysis.

Computer science-Mathematics.

Discrete mathematics.

Computer Vision.

Automated Pattern Recognition.

Functional Analysis.

Discrete Mathematics in Computer Science.

Local Subjects:

Computer Vision.

Automated Pattern Recognition.

Functional Analysis.

Discrete Mathematics in Computer Science.

Physical Description:

1 online resource (XX, 144 pages) : 31 illustrations, 5 illustrations in color.

Edition:

1st ed. 2020.

Contained In:

Springer Nature eBook

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2020.

System Details:

text file PDF

Summary:

This book discusses different types of distance functions defined in an n-D integral space for their usefulness in approximating the Euclidean metric. It discusses the properties of these distance functions and presents various kinds of error analysis in approximating Euclidean metrics. It also presents a historical perspective on efforts and motivation for approximating Euclidean metrics by digital distances from the mid-sixties of the previous century. The book also contains an in-depth presentation of recent progress, and new research problems in this area. .

Contents:

Geometry, Space and Metrics

Digital distances: Classes and hierarchies

Error analysis analytical approaches

Linear combination of digital distances.

Other Format:

Printed edition:

ISBN:

978-981-15-9901-9

9789811599019

Access Restriction:

Restricted for use by site license.