QR code localization using boosted cascade of weak classifiers

Péter Bodnár, László G. Nyúl

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Usage of computer-readable visual codes became common in our everyday life at industrial environments and private use. The reading process of visual codes consists of two steps: localization and data decoding. Unsupervised localization is desirable at industrial setups and for visually impaired people. This paper examines localization efficiency of cascade classifiers using Haar-like features, Local Binary Patterns and Histograms of Oriented Gradients, trained for the finder patterns of QR codes and for the whole code region as well, and proposes improvements in post-processing.

Original languageEnglish
Pages (from-to)338-345
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8814
DOIs
Publication statusPublished - Jan 1 2014

Keywords

  • Cascade classifier
  • HAAR
  • HOG
  • LBP
  • Object detection
  • QR code

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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