English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
 Local TagsRelease HistoryDetailsSummary
  Optical Flow Estimation with Channel Constancy

Sevilla-Lara, L., Sun, D., Learned-Miller, E. G., & Black, M. J. (2014). Optical Flow Estimation with Channel Constancy. In D. Fleet, T. Pajdla, B. Schiele, & T. Tuytelaars (Eds.), Computer Vision - ECCV 2014. Proceedings, Part 1 (pp. 423-438). Cham et al.: Springer International Publishing. doi:10.1007/978-3-319-10590-1_28.

Item is

 

show all hide all

Basic

show hide
Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0024-E2F1-0 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0024-E2F2-E
Genre: Conference Paper

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Sevilla-Lara, Laura1, Author           
Sun, Deqing, Author
Learned-Miller, Erik G., Author
Black, Michael J.1, Author           
Affiliations:
1Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497642              

Content

show
hide
Free keywords: Abt. Black
 Abstract: Large motions remain a challenge for current optical flow algorithms. Traditionally, large motions are addressed using multi-resolution representations like Gaussian pyramids. To deal with large displacements, many pyramid levels are needed and, if an object is small, it may be invisible at the highest levels. To address this we decompose images using a channel representation (CR) and replace the standard brightness constancy assumption with a descriptor constancy assumption. CRs can be seen as an over-segmentation of the scene into layers based on some image feature. If the appearance of a foreground object differs from the background then its descriptor will be different and they will be represented in different layers.We create a pyramid by smoothing these layers, without mixing foreground and background or losing small objects. Our method estimates more accurate flow than the baseline on the MPI-Sintel benchmark, especially for fast motions and near motion boundaries.

Details

show
hide
Language(s):
 Dates: 2014-09
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-319-10590-1_28
BibTex Citekey: Sevilla:ECCV:2014
 Degree: -

Event

show
hide
Title: ECCV 2014 - 13th European Conference on Computer Vision
Place of Event: Zürich
Start-/End Date: 2014-09-06 - 2014-09-12

Legal Case

show

Project information

show

Source 1

show
hide
Title: Computer Vision - ECCV 2014. Proceedings, Part 1
Source Genre: Proceedings
 Creator(s):
Fleet, D., Editor
Pajdla, T., Editor
Schiele, B., Editor
Tuytelaars, T., Editor
Affiliations:
-
Publ. Info: Cham et al. : Springer International Publishing
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 423 - 438 Identifier: ISBN: 978-3-319-10589-5
ISBN: 978-3-319-10590-1

Source 2

show
hide
Title: Lecture Notes in Computer Science
Source Genre: Series
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 8689 Sequence Number: - Start / End Page: - Identifier: -