Dense Image Correspondences for Computer Vision

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 applic...

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Détails bibliographiques
Cote:Libro Electrónico
Collectivité auteur: SpringerLink (Online service)
Autres auteurs: Hassner, Tal (Éditeur intellectuel), Liu, Ce (Éditeur intellectuel)
Format: Électronique eBook
Langue:Inglés
Publié: Cham : Springer International Publishing : Imprint: Springer, 2016.
Édition:1st ed. 2016.
Sujets:
Signal processing.
Computer vision.
Artificial intelligence.
Telecommunication.
Signal, Speech and Image Processing .
Computer Vision.
Artificial Intelligence.
Communications Engineering, Networks.
Accès en ligne:Texto Completo
Table des matières:
  • 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.

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