The Resource Dense image correspondences for computer vision, Tal Hassner, Ce Liu, editors

Dense image correspondences for computer vision, Tal Hassner, Ce Liu, editors

Label
Dense image correspondences for computer vision
Title
Dense image correspondences for computer vision
Statement of responsibility
Tal Hassner, Ce Liu, editors
Contributor
Editor
Subject
Genre
Language
eng
Summary
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
Cataloging source
N$T
Dewey number
621.3
Index
no index present
LC call number
TK153
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
  • Hassner, Tal
  • Liu, Ce
http://library.link/vocab/subjectName
  • Electrical engineering
  • Computer monitors
  • Image processing
  • TECHNOLOGY & ENGINEERING
  • Electrical engineering
  • Image processing
  • Engineering
  • Signal, Image and Speech Processing
  • Image Processing and Computer Vision
  • Artificial Intelligence (incl. Robotics)
  • Communications Engineering, Networks
  • Image processing
  • Artificial intelligence
  • Communications engineering / telecommunications
  • Imaging systems & technology
Label
Dense image correspondences for computer vision, Tal Hassner, Ce Liu, editors
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-319-23048-1
Instantiates
Publication
Copyright
Antecedent source
unknown
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
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
Dimensions
unknown
Extent
1 online resource
File format
unknown
Form of item
online
Isbn
9783319230481
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Note
SpringerLink
Other control number
10.1007/978-3-319-23048-1
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • (OCoLC)930268879
  • (OCoLC)ocn930268879
Label
Dense image correspondences for computer vision, Tal Hassner, Ce Liu, editors
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-319-23048-1
Publication
Copyright
Antecedent source
unknown
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
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
Dimensions
unknown
Extent
1 online resource
File format
unknown
Form of item
online
Isbn
9783319230481
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Note
SpringerLink
Other control number
10.1007/978-3-319-23048-1
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • (OCoLC)930268879
  • (OCoLC)ocn930268879

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