Image edge detection based on a spatial autoregressive bootstrap approach

Gustavo Ulloa, HÉCTOR GABRIEL ALLENDE CID, Héctor Allende

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

In this paper a new algorithm to perform edge detection based on a bootstrap approach is presented. This approach uses the estimated spatial conditional distribution of the pixels conditioned by their neighbors. The proposed algorithm approximates the original image by adjusting local 2D autoregressive models to different blocks of the image. The residuals are used in order to generate resampled images by using bootstrap techniques. The proposed algorithm applied to synthetic and real images generates as a result, a binary image, in which the detected edges can be observed.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsAlvaro Pardo, Josef Kittler
PublisherSpringer Verlag
Pages408-415
Number of pages8
ISBN (Print)9783319257501
DOIs
StatePublished - 2015
Externally publishedYes
Event20th Iberoamerican Congress on on Pattern Recognition, CIARP 2015 - Montevideo, Uruguay
Duration: 9 Nov 201512 Nov 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9423
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th Iberoamerican Congress on on Pattern Recognition, CIARP 2015
CountryUruguay
CityMontevideo
Period9/11/1512/11/15

Keywords

  • Edge detection
  • Image procesing
  • Segmentation
  • Sieve bootstrap
  • Spatial autoregressive models

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