On Curve Matching (Classic Reprint)

Haim Wolfson

ISBN 10: 1334017131 / ISBN 13: 9781334017131

Published by Forgotten Books, 2024

About this Item

Print on Demand. This book introduces three algorithms designed to find the longest common subcurve of two curves. It begins with the concept of converting curves into sequences of real numbers that represent local characteristics of the curve, such as curvature. Then the author discusses applying string-matching techniques to these sequences to find the longest common substring, which can then be mapped back to the original curves to identify the matching subcurves. The algorithms presented here improve upon existing techniques by offering increased efficiency and robustness, with one algorithm achieving a complexity of O(n), where n is the number of sample points on the longer curve. The author provides an in-depth analysis of the complexity and robustness of each algorithm, and concludes with experimental results demonstrating the effectiveness of these techniques in practice. This book is a reproduction of an important historical work, digitally reconstructed using state-of-the-art technology to preserve the original format. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in the book.

Seller Inventory # 9781334017131_0

Bibliographic Details

• Title: On Curve Matching (Classic Reprint)
• Publisher: Forgotten Books
• Publication Date: 2024
• Language: English
• ISBN 10: 1334017131
• ISBN 13: 9781334017131
• Binding: Paperback
• Condition: New
• Book Type: print-on-demand item
• Seller catalogs:
  • Calculus

About this title

Synopsis

This book introduces three algorithms designed to find the longest common subcurve of two curves. It begins with the concept of converting curves into sequences of real numbers that represent local characteristics of the curve, such as curvature. Then the author discusses applying string-matching techniques to these sequences to find the longest common substring, which can then be mapped back to the original curves to identify the matching subcurves. The algorithms presented here improve upon existing techniques by offering increased efficiency and robustness, with one algorithm achieving a complexity of O(n), where n is the number of sample points on the longer curve. The author provides an in-depth analysis of the complexity and robustness of each algorithm, and concludes with experimental results demonstrating the effectiveness of these techniques in practice.