机构地区：[1]Laboratoire d’Instrumentation, Image et Spectroscopie (L2IS), Institut National Polytechnique-Felix Houphouët Boigny, Yamoussoukro, Côte d’Ivoire [2]Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), Université d’Angers, Institut Universitaire de Technologie, 4 Bd Lavoisier, Angers Cedex, France [3]Institut des Sciences et Technologies Moléculaires d’Angers (MOLTECH ANJOU) UMR CNRS 6200, Université d’Angers, 2 Bd Lavoisier, Angers Cedex 2, France
出 处：《Journal of Signal and Information Processing》2020年第3期58-73,共16页信号与信息处理（英文）
摘 要：Local Binary Patterns (LBPs) have been highly used in texture classification <span style="font-family:Verdana;">for their robustness, their ease of implementation an</span><span style="font-family:Verdana;">d their low computational</span><span style="font-family:;" "=""> </span><span style="font-family:;" "=""><span style="font-family:Verdana;">cost. Initially designed to deal with gray level images, several methods based on them in the literature have been proposed for images having more than one spectral band. To achieve it, whether assumption using color information or combining spectral band two by two was done. Those methods use micro </span><span style="font-family:Verdana;">structures as texture features. In this paper, our goal was to design texture features which are relevant to color and multicomponent texture analysi</span><span style="font-family:Verdana;">s withou</span><span style="font-family:Verdana;">t any assumption.</span></span><span style="font-family:;" "=""> </span><span style="font-family:;" "=""><span style="font-family:Verdana;">Based on methods designed for gray scale images, we find the combination of micro and macro structures efficient for multispectral texture analysis. The experimentations were carried out on color images from Outex databases and multicomponent images from red blood cells captured using a multispectral microscope equipped with 13 LEDs ranging </span><span style="font-family:Verdana;">from 375 nm to 940 nm. In all achieved experimentations, our propos</span><span style="font-family:Verdana;">al presents the best classification scores compared to common multicomponent LBP methods.</span></span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">99.81%, 100.00%,</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">99.07% and 97.67% are</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">maximum scores obtained with our strategy respectively applied to images subject to rotation, blur, Local Binary Patterns (LBPs) have been highly used in texture classification <span style="font-family:Verdana;">for their robustness, their ease of implementation an</span><span style="font-family:Verdana;">d their low computational</span><span style="font-family:;" "=""> </span><span style="font-family:;" "=""><span style="font-family:Verdana;">cost. Initially designed to deal with gray level images, several methods based on them in the literature have been proposed for images having more than one spectral band. To achieve it, whether assumption using color information or combining spectral band two by two was done. Those methods use micro </span><span style="font-family:Verdana;">structures as texture features. In this paper, our goal was to design texture features which are relevant to color and multicomponent texture analysi</span><span style="font-family:Verdana;">s withou</span><span style="font-family:Verdana;">t any assumption.</span></span><span style="font-family:;" "=""> </span><span style="font-family:;" "=""><span style="font-family:Verdana;">Based on methods designed for gray scale images, we find the combination of micro and macro structures efficient for multispectral texture analysis. The experimentations were carried out on color images from Outex databases and multicomponent images from red blood cells captured using a multispectral microscope equipped with 13 LEDs ranging </span><span style="font-family:Verdana;">from 375 nm to 940 nm. In all achieved experimentations, our propos</span><span style="font-family:Verdana;">al presents the best classification scores compared to common multicomponent LBP methods.</span></span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">99.81%, 100.00%,</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">99.07% and 97.67% are</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">maximum scores obtained with our strategy respectively applied to images subject to rotation, blur,
关 键 词：Multispectral Images Local Binary Patterns (LBP) Texture Analysis Rotation Invariance Illumination Variation Blurring Invariance
分 类 号：TP3[自动化与计算机技术—计算机科学与技术]
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