Scale estimation with difference of ordered residuals

Maria Scalzo-Cornacchia, Senem Velipasalar

Research output: Chapter in Book/Entry/PoemConference contribution


Multiple model estimation is an important problem in computer vision. Through estimation, one can detect important structural information in an image. A crucial step in multiple model estimation is the ability to dichotomize inliers of a model from outliers. This paper proposes a novel technique for estimating the scale of a model. In contrast to previous adaptive scale estimate works, our method removes the need for user provided input. We achieve accurate scale estimation through consecutive inspection of the ordered residuals. Our results show the ability of the proposed scale estimate metric to maintain accurate scale estimation even with over 90% outliers present in the data. Likewise, we also apply our scale estimator with multiple model estimation problems for detecting planes and two-view motions, demonstrating the ability of our approach to accurately estimate scale in real application oriented scenarios.

Original languageEnglish (US)
Title of host publicationProceedings - International Conference on Image Processing, ICIP
PublisherIEEE Computer Society
Number of pages5
ISBN (Print)9781479983391
StatePublished - Dec 9 2015
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: Sep 27 2015Sep 30 2015


OtherIEEE International Conference on Image Processing, ICIP 2015
CityQuebec City

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


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