An object detection approach for cluttered images

Description

Thesis (MScEng)--Stellenbosch University, 2003. / ENGLISH ABSTRACT: We investigate object detection against cluttered backgrounds, based on the MINACE
(Minimum Noise and Correlation Energy) filter. Application of the filter is followed
by a suitable segmentation algorithm, and the standard techniques of global and local
thresholding are compared to watershed-based segmentation. The aim of this approach is
to provide a custom region-based object detection algorithm with a concise set of regions
of interest.
Two industrial case studies are examined: diamond detection in X-ray images, and the
reading of a dynamic, and ink stamped, 2D barcode on packaging clutter. We demonstrate
the robustness of our approach on these two diverse applications, and develop a complete
algorithmic prototype for an automatic stamped code reader. / AFRIKAANSE OPSOMMING: Hierdie tesis ondersoek die herkenning van voorwerpe teen onduidelike agtergronde. Ons
benadering maak staat op die MINACE (" Minimum Noise and Correlation Energy") korrelasiefilter.
Die filter word aangewend saam met 'n gepaste segmenteringsalgoritme, en
die standaard tegnieke van globale en lokale drumpelingsalgoritmes word vergelyk met 'n
waterskeidingsgebaseerde segmenteringsalgoritme. Die doel van hierdie deteksiebenadering
is om 'n klein stel moontlike voorwerpe te kan verskaf aan enige klassifikasie-algoritme
wat fokus op die voorwerpe self.
Twee industriële toepassings word ondersoek: die opsporing van diamante in X-straal
beelde, en die lees van 'n dinamiese, inkgedrukte, 2D balkieskode op verpakkingsmateriaal.
Ons demonstreer die robuustheid van ons benadering met hierdie twee uiteenlopende
voorbeelde, en ontwikkel 'n volledige algoritmiese prototipe vir 'n outomatiese
stempelkode leser.

Links & Downloads

1. http://hdl.handle.net/10019.1/53281

Tags

Image processing -- Digital techniques

Dissertations -- Electronic engineering

Additional Fields

Identifer	oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/53281
Date	12 1900
Creators	Kok, R.
Contributors	Herbst, Ben, Lourens, J. G., Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.
Publisher	Stellenbosch : Stellenbosch University
Source Sets	South African National ETD Portal
Language	en_ZA
Detected Language	English
Type	Thesis
Format	115 p. : ill.
Rights	Stellenbosch University