An object detection approach for cluttered images

Kok, R. (2003-12)

Thesis (MScEng)--Stellenbosch University, 2003.

Thesis

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.

Please refer to this item in SUNScholar by using the following persistent URL: http://hdl.handle.net/10019.1/53281
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