Cantitate/Preț
Produs

Dense Image Correspondences for Computer Vision

Editat de Tal Hassner, Ce Liu
en Limba Engleză Hardback – 2 dec 2015
This book describes the fundamental building-block of many new computer vision systems: dense and robust correspondence estimation. Dense correspondence estimation techniques are now successfully being used to solve a wide range of computer vision problems, very different from the traditional applications such techniques were originally developed to solve. This book introduces the techniques used for establishing correspondences between challenging image pairs, the novel features used to make these techniques robust, and the many problems dense correspondences are now being used to solve. The book provides information to anyone attempting to utilize dense correspondences in order to solve new or existing computer vision problems. The editors describe how to solve many computer vision problems by using dense correspondence estimation. Finally, it surveys resources, code and data, necessary for expediting the development of effective correspondence-based computer vision systems.
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (1) 68336 lei  6-8 săpt.
  Springer International Publishing – 23 aug 2016 68336 lei  6-8 săpt.
Hardback (1) 63418 lei  6-8 săpt.
  Springer International Publishing – 2 dec 2015 63418 lei  6-8 săpt.

Preț: 63418 lei

Preț vechi: 74609 lei
-15% Nou

Puncte Express: 951

Preț estimativ în valută:
12138 12734$ 10069£

Carte tipărită la comandă

Livrare economică 29 ianuarie-12 februarie 25

Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9783319230474
ISBN-10: 3319230476
Pagini: 295
Ilustrații: XII, 295 p. 152 illus., 146 illus. in color.
Dimensiuni: 155 x 235 x 22 mm
Greutate: 0.61 kg
Ediția:1st ed. 2016
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland

Public țintă

Professional/practitioner

Cuprins

Introduction to Dense Optical Flow.- SIFT Flow: Dense Correspondence across Scenes and its Applications.- Dense, Scale-Less Descriptors.- Scale-Space SIFT Flow.- Dense Segmentation-aware Descriptors.- SIFTpack: A Compact Representation for Efficient SIFT Matching.- In Defense of Gradient-Based Alignment on Densely Sampled Sparse Features.- From Images to Depths and Back.- DepthTransfer: Depth Extraction from Video Using Non-parametric Sampling.- Joint Inference in Image Datasets via Dense Correspondence.- Dense Correspondences and Ancient Texts.

Notă biografică

Prof. Tal Hassner is a faculty member of the Department of Mathematics and Computer Science, The Open University of Israel, Israel. Ce Liu is a Researcher with Google.

Textul de pe ultima copertă

This book describes the fundamental building-block of many new computer vision systems: dense and robust correspondence estimation. Dense correspondence estimation techniques are now successfully being used to solve a wide range of computer vision problems, very different from the traditional applications such techniques were originally developed to solve. This book introduces the techniques used for establishing correspondences between challenging image pairs, the novel features used to make these techniques robust, and the many problems dense correspondences are now being used to solve. The book provides information to anyone attempting to utilize dense correspondences in order to solve new or existing computer vision problems. The editors describe how to solve many computer vision problems by using dense correspondence estimation. Finally, it surveys resources, code, and data necessary for expediting the development of effective correspondence-based computer vision systems.
 
·         Provides in-depth coverage of dense-correspondence estimation
·         Covers both the breadth and depth of new achievements in dense correspondence estimation and their applications
·         Includes information for designing computer vision systems that rely on efficient and robust correspondence estimation
 

Caracteristici

Provides in-depth coverage of dense-correspondence estimation Covers both the breadth and depth of new achievements in dense correspondence estimation and their applications Includes information for designing computer vision systems which rely on efficient and robust correspondence estimation