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...

Full description

Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Hassner, Tal (Editor), Liu, Ce (Editor)
Format: e-Book
Language:English
Published: Cham : Springer International Publishing : 2016.
Imprint: Springer,
Edition:1st ed. 2016.
Subjects:
Signal processing.
Computer vision.
Artificial intelligence.
Telecommunication.
Signal, Speech and Image Processing .
Computer Vision.
Artificial Intelligence.
Communications Engineering, Networks.
Online Access:Full-text access
Table of 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.

Similar Items