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by Kenichi Kanatani

  • ISBN: 0387512535
  • Category: Math & Science
  • Author: Kenichi Kanatani
  • Subcategory: Mathematics
  • Other formats: txt lrf lrf rtf
  • Language: English
  • Publisher: Springer Verlag (May 1, 1990)
  • Pages: 459 pages
  • FB2 size: 1427 kb
  • EPUB size: 1923 kb
  • Rating: 4.8
  • Votes: 585
Download Group Theoretical Methods in Image Understanding (Springer Series in Information Sciences) fb2

Authors: Kanatani, Kenichi. Image understanding is an attempt to extract knowledge about a 3D scene from 20 images. Bibliographic Information. Group-Theoretical Methods in Image Understanding.

Authors: Kanatani, Kenichi. The recent development of computers has made it possible to automate a wide range of systems and operations, not only in the industry, military, and special environments (space, sea, atomic plants, et., but also in daily life.

Series: Springer Series in Information Sciences (Book 20). Paperback: 459 pages. Tell the Publisher! I'd like to read this book on Kindle.

Электронная книга "Group-Theoretical Methods in Image Understanding", Ken-ichi Kanatani. Эту книгу можно прочитать в Google Play Книгах на компьютере, а также на устройствах Android и iOS. Выделяйте текст, добавляйте закладки и делайте заметки, скачав книгу "Group-Theoretical Methods in Image Understanding" для чтения в офлайн-режиме.

Start by marking Group Theoretical Methods In Image Understanding as Want to Read . This book presents the mathematics relevant to image understanding by computer vision and gives examples of actual applications

Start by marking Group Theoretical Methods In Image Understanding as Want to Read: Want to Read savin. ant to Read. This book presents the mathematics relevant to image understanding by computer vision and gives examples of actual applications. Group representation theory, Lie groups and Lie algebras, the theory of invariance, tensor calculus, differential geometry and projective geometry are used for three-dimensional shape and motion analysis from images, making use of techniques such This book presents the mathematics relevant to image understanding by computer vision and gives examples of actual applications.

Springer, Berlin (1990). Explore Further: Topics Discussed in This Paper. International Standard Book Number. 459 p. DM 11. 0 (cloth), ISBN no: 3-540-51253-5}, author {Bao-Zong Yuan}, year {1991} }. Bao-Zong Yuan. Topics from this paper. Information Sciences.

anjelicajohnson53 anjelicajohnson53. Hatboro (images of america). Hanover, new hampshire: volume ii (images of america).

Tell us if something is incorrect. We aim to show you accurate product information. Manufacturers, suppliers and others provide what you see here, and we have not verified it. See our disclaimer. Paperback, Springer Verlag, 2012, ISBN13 9783642647727, ISBN10 3642647723. Springer Series in Information Sciences.

This is a list of important publications in theoretical computer science, organized by field. Some reasons why a particular publication might be regarded as important: Topic creator – A publication that created a new topic. Breakthrough – A publication that changed scientific knowledge significantly. Influence – A publication which has significantly influenced the world or has had a massive impact on the teaching of theoretical computer science.

Springer Texts in Statistics. Statistical learning refers to a set of tools for modeling and understanding complex datasets. Gareth James, Daniela Witten, Trevor Hastie Robert Tibshirani. An Introduction to Statistical Learning. The publisher makes no warranty, express or implied, with respect to the material contained herein.

This book presents the mathematics relevant to image understanding by computer vision and gives examples of actual applications. Group representation theory, Lie groups and Lie algebras, the theory of invariance, tensor calculus, differential geometry and projective geometry are used for three-dimensional shape and motion analysis from images, making use of techniques such as shape from motion, shape from texture, shape from angle and shape from surface. Although the mathematics itself may be well known to mathematicians, people working in areas related to computer science, image understanding, computer vision and image processing have usually never studied such mathematics, and so may be surprised to learn that abstract mathematical concepts can be of enormous help in building intelligent computer vision systems.

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