<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Melinda Katona</style></author><author><style face="normal" font="default" size="100%">László Gábor Nyúl</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Kokou Yetongnon</style></author><author><style face="normal" font="default" size="100%">Richard Chbeir</style></author><author><style face="normal" font="default" size="100%">Albert Dipanda</style></author><author><style face="normal" font="default" size="100%">Luigi Gallo</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A novel method for accurate and efficient barcode detection with morphological operations</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the International Conference on Signal Image Technology &amp; Internet Systems (SITIS)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Nov 2012</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Naples, Italy</style></pub-location><pages><style face="normal" font="default" size="100%">307 - 314</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4673-5152-2 </style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div class=&quot;article&quot;&gt;&lt;p&gt;&lt;span class=&quot;snippet&quot;&gt;Barcode&lt;/span&gt; technology is the pillar of automatic identification, that is used in a wide range of real-time applications with various types of codes. The different types of codes and applications impose special problems, so there is a continuous need for solutions with improved effectiveness. There are several &lt;span class=&quot;snippet&quot;&gt;methods&lt;/span&gt; for &lt;span class=&quot;snippet&quot;&gt;barcode&lt;/span&gt; localization, that are well characterized by accuracy and speed. Particularly, high-speed processing places need automatic &lt;span class=&quot;snippet&quot;&gt;barcode&lt;/span&gt; localization, e.g. conveyor belts, automated production, where missed &lt;span class=&quot;snippet&quot;&gt;detections&lt;/span&gt; cause loss of profit. In this paper, we mainly deal with segmentation of images with 1D &lt;span class=&quot;snippet&quot;&gt;barcode&lt;/span&gt;, but also analyze the &lt;span class=&quot;snippet&quot;&gt;operation&lt;/span&gt; of different &lt;span class=&quot;snippet&quot;&gt;methods&lt;/span&gt; for 2D &lt;span class=&quot;snippet&quot;&gt;barcode&lt;/span&gt; images as well. Our goal is to detect automatically, rapidly and accurately the &lt;span class=&quot;snippet&quot;&gt;barcode&lt;/span&gt; location by the help of extracted features. We compare some published &lt;span class=&quot;snippet&quot;&gt;method&lt;/span&gt; from the literature, which basically rely on the contrast between the background and the shape that represent the code. We also propose a &lt;span class=&quot;snippet&quot;&gt;novel&lt;/span&gt; algorithm, that outperforms the others in both accuracy and efficiency in detecting 1D codes.&lt;/p&gt;&lt;/div&gt;&lt;p&gt;&amp;nbsp;&lt;/p&gt;</style></abstract><work-type><style face="normal" font="default" size="100%">Conference paper</style></work-type><accession-num><style face="normal" font="default" size="100%">13227629</style></accession-num><notes><style face="normal" font="default" size="100%">ScopusID: 84874042343doi: 10.1109/SITIS.2012.53</style></notes></record></records></xml>