Extracting good features for motion estimation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Selecting image features whose correspondences can be accurately established between images is a key step in many image processing problems, such as camera and object motion estimation, 3D structure reconstruction, and image registration. In this paper, we present a new method of selecting good features for estimating motion from images. Our approach is different from other existing approaches in that we formulate feature tracking as a signal parameter estimation problem, give a quantitative measure of feature quality in terms of how accurately the feature can be tracked, and can adaptively select features with different shapes and sizes which depend on the local variations of the images. Through the analysis of this feature quality measure, we can characterize the basic properties that allow a feature to be well tracked. Some experimental results are shown to demonstrate the advantages and robustness of the proposed method.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Image Processing
Editors Anon
PublisherIEEE
Pages117-120
Number of pages4
Volume1
StatePublished - Dec 1 1996
EventProceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3) - Lausanne, Switz
Duration: Sep 16 1996Sep 19 1996

Other

OtherProceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3)
CityLausanne, Switz
Period9/16/969/19/96

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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    Tan, Y. P., Kulkarni, S. R., & Ramadge, P. J. (1996). Extracting good features for motion estimation. In Anon (Ed.), IEEE International Conference on Image Processing (Vol. 1, pp. 117-120). IEEE.