Object mark segmentation algorithm using Dynamic Programming for poor quality images in automated inspection process

Dong Joong Kang, Jong Eun Ha, In Mo Ahn

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

Abstract

This paper presents a method to segment object ID (identification) marks on poor quality images under uncontrolled lighting conditions of automated inspection process. The method is based on multiple templates and normalized gray-level correlation (NGC) method. We propose a multiple template method, called as ATM (Active Template Model) which uses a search technique of multiple templates from model templates to match and segment character regions of the inspection images. Conventional Snakes algorithm provides a good methodology to model the functional of ATM. To increase the computation speed to segment the ID mark regions, we introduce the Dynamic Programming based algorithm. Experimental results using real images from automated factory are presented.

Original languageEnglish
Title of host publicationComputational Science and Its Applications - ICCSA 2004 - International Conference, Proceedings
PublisherSpringer Verlag
Pages896-905
Number of pages10
EditionPART 4
ISBN (Print)3540220607, 9783540220602
DOIs
StatePublished - 2004
EventInternational Conference on Computational Science and Its Applications, ICCSA 2004 - Assisi, Italy
Duration: 14 May 200417 May 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 4
Volume3046 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Computational Science and Its Applications, ICCSA 2004
Country/TerritoryItaly
CityAssisi
Period14/05/0417/05/04

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