<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Boudra, Safia</style></author><author><style face="normal" font="default" size="100%">Yahiaoui, Itheri</style></author><author><style face="normal" font="default" size="100%">Behloul, Ali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Tree trunk texture classification using multi-scale statistical macro binary patterns and CNN</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Soft Computing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2022</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1016/j.asoc.2022.108473</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">118</style></volume><pages><style face="normal" font="default" size="100%">108473</style></pages><isbn><style face="normal" font="default" size="100%">1568-4946</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Automated plant&amp;nbsp;classification&amp;nbsp;using tree trunk has attracted increasing interest in the&amp;nbsp;computer vision&amp;nbsp;community as a contributed solution for the management of biodiversity. It is based on the description of the texture information of the bark surface. The multi-scale variants of the&amp;nbsp;local binary patterns&amp;nbsp;have achieved prominent performance in bark texture description. However, these approaches encode the scale levels of the macrostructure separately from each other. In this paper, a novel handcrafted&amp;nbsp;texture descriptor&amp;nbsp;termed multi-scale Statistical Macro Binary Patterns (ms-SMBP) is proposed to encode the characterizing macro pattern of different bark species. The proposed approach consists of defining a sampling scheme at high scale levels and summarizing the intensity distribution using statistical measures. The characterizing macro pattern is encoded by an in-depth gradient that describes the relationship between the scale levels and their adaptive statistical prototype. Besides this handcrafted feature descriptor, a learning-based description is performed with the ResNet34 model for bark classification. Extensive and comprehensive experiments on challenging and large-scale bark datasets demonstrate the effectiveness of ms-SMBP to identify bark species and outperforming different multi-scale LBP approaches. The tree trunk classification with ResNet34 shows interesting results on a very large-scale dataset.
&lt;/p&gt;
</style></abstract></record></records></xml>