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                <identifier>ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:1:6366</identifier>
                <datestamp>2018-11-17T17:56:54Z</datestamp>
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                    <dim:field mdschema="dc" element="title" lang="en">Link Shifting Based Pyramid Segmentation for Elongated Regions</dim:field>
                    <dim:field mdschema="dc" element="date" qualifier="issued">2009</dim:field>
                    <dim:field mdschema="dc" element="identifier" qualifier="uri">http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/1/6366</dim:field>
                    <dim:field mdschema="dc" element="identifier" qualifier="uri">https://link.springer.com/chapter/10.1007/978-3-642-10546-3_18</dim:field>
                    <dim:field mdschema="dc" element="contributor" qualifier="author" authority="orcid::0000-0001-6279-2988" confidence="-1">M. Stojmenović</dim:field>
                    <dim:field mdschema="dc" element="contributor" qualifier="author" authority="id:24016" confidence="-1">A. Solis Montero</dim:field>
                    <dim:field mdschema="dc" element="contributor" qualifier="author" authority="id:24017" confidence="-1">A. Nayak</dim:field>
                    <dim:field mdschema="dc" element="description" qualifier="abstract">The goal of image segmentation is to partition an image into regions
that are internally homogeneous and heterogeneous with respect to other
neighbouring regions. An improvement to the regular pyramid image segmentation
scheme is presented here which results in the correct segmentation of
elongated and large regions. The improvement is in the way child nodes choose
parents. Each child node considers the neighbours of its candidate parent, and
the candidate parents of its neighbouring nodes in the same level alongside the
standard candidate nodes in the layer above. We also modified a tie-breaking
rule for selecting the parent node. It concentrates around single parent nodes
when alternatives do not differ significantly. Images were traversed top to bottom,
left to right in odd iterations, and bottom to top, right to left in even iterations,
which improved the speed at which accurate segmentation was achieved.
The new algorithm is tested on a set of images.</dim:field>
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                    <dim:field mdschema="dc" element="citation" qualifier="spage">141</dim:field>
                    <dim:field mdschema="dc" element="citation" qualifier="epage">152</dim:field>
                    <dim:field mdschema="dc" element="source">Signal Processing, Image Processing and Pattern Recognition (SIP 2009)</dim:field>
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