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		<citationkey>MirandaBergRochFalc:2006:NeAlCo</citationkey>
		<title>Tree-Pruning: A New Algorithm and Its Comparative Analysis with theWatershed Transform for Automatic Image Segmentation</title>
		<format>On-line</format>
		<year>2006</year>
		<date>8-11 Oct. 2006</date>
		<numberoffiles>1</numberoffiles>
		<size>573 KiB</size>
		<author>Miranda, Paulo André Vechiatto de,</author>
		<author>Bergo, Felipe Paulo Guazzi,</author>
		<author>Rocha, Leonardo Marques,</author>
		<author>Falcao, Alexandre Xavier,</author>
		<affiliation>LIV, Institute of Computing, State University of Campinas</affiliation>
		<affiliation>LIV, Institute of Computing, State University of Campinas</affiliation>
		<affiliation>DECOM, FEEC, State University of Campinas</affiliation>
		<affiliation>LIV, Institute of Computing, State University of Campinas</affiliation>
		<editor>Oliveira Neto, Manuel Menezes de,</editor>
		<editor>Carceroni, Rodrigo Lima,</editor>
		<e-mailaddress>afalcao@ic.unicamp.br</e-mailaddress>
		<conferencename>Brazilian Symposium on Computer Graphics and Image Processing, 19 (SIBGRAPI)</conferencename>
		<conferencelocation>Manaus</conferencelocation>
		<booktitle>Proceedings</booktitle>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<tertiarytype>Full Paper</tertiarytype>
		<keywords>image segmentation, license plate detection, image foresting transform, watershed transform, image analysis.</keywords>
		<abstract>Image segmentation using tree pruning (TP) and watershed (WS) has been presented in the framework of the image forest transform (IFT) a method to reduce image processing problems related to connectivity into an optimumpath forest problem in a graph. Given that both algorithms use the IFT with similar parameters, they usually produce similar segmentation results. However, they rely on different properties of the IFT which make TP more robust than WS for automatic segmentation tasks. We propose and demonstrate an important improvement in the TP algorithm, clarify the differences between TP and WS, and provide their comparative analysis from the theoretical and practical points of view. The experiments involve automatic segmentation of license plates in a database with 990 images.</abstract>
		<language>en</language>
		<targetfile>miranda-treepruning.pdf</targetfile>
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