In this paper, a method and algorithm of skeleton extraction based on binary mathematical morphology is presented. Sequential structuring elements (SEs) is also studied, which is the key problem of skeleton extraction...In this paper, a method and algorithm of skeleton extraction based on binary mathematical morphology is presented. Sequential structuring elements (SEs) is also studied, which is the key problem of skeleton extraction. The examples of boiler flame image processing show that the detected skeletons can present the geometric shape of flame images well.展开更多
The accurate identification of various postures in the daily life of piglets that are directly reflected by their skeleton morphology is necessary to study the behavioral characteristics of pigs.Accordingly,this study...The accurate identification of various postures in the daily life of piglets that are directly reflected by their skeleton morphology is necessary to study the behavioral characteristics of pigs.Accordingly,this study proposed a novel approach for the skeleton extraction and pose estimation of piglets.First,an improved Zhang-Suen(ZS)thinning algorithm based on morphology was used to establish the chain code mechanism of the burr and the redundant information deletion templates to achieve a single-pixel width extraction of pig skeletons.Then,body nodes were extracted on the basis of the improved DeepLabCut(DLC)algorithm,and a part affinity field(PAF)was added to realize the connection of body nodes,and consequently,construct a database of pig behavior and postures.Finally,a support vector machine was used for pose matching to recognize the main behavior of piglets.In this study,14000 images of piglets with different types of behavior were used in posture recognition experiments.Results showed that the improved algorithm based on ZS-DLC-PAF achieved the best thinning rate compared with those of distance transformation,medial axis transformation,morphology refinement,and the traditional ZS algorithm.The node tracking accuracy reached 85.08%,and the pressure test could accurately detect up to 35 nodes of 5 pigs.The average accuracy of posture matching was 89.60%.This study not only realized the single-pixel extraction of piglets’skeletons but also the connection among the different behavior body nodes of individual sows and multiple piglets.Furthermore,this study established a database of pig posture behavior,which provides a reference for studying animal behavior identification and classification and anomaly detection.展开更多
针对树木三维重构过程中面临的处理速度慢、重构精度低等问题,提出一种采用激光点云数据的单木骨架三维重构方法。首先,根据点云数据类型确定组合滤波方式,以去除离群点和地面点;其次,采用一种基于内部形态描述子(ISS)和相干点漂移算法(...针对树木三维重构过程中面临的处理速度慢、重构精度低等问题,提出一种采用激光点云数据的单木骨架三维重构方法。首先,根据点云数据类型确定组合滤波方式,以去除离群点和地面点;其次,采用一种基于内部形态描述子(ISS)和相干点漂移算法(CPD)的混合配准算法(Intrinsic Shape-Coherent Point Drift,IS-CPD),以获取单棵树木的完整点云数据;最后,采用Laplace收缩点集和拓扑细化相结合的方法提取骨架,并通过柱体构建枝干模型,实现骨架三维重构。试验结果表明,相比传统CPD算法,研究设计的配准方案精度和执行速度分别提高50%和95.8%,最终重构误差不超过2.48%。研究结果证明可有效地重构单棵树木的三维骨架,效果接近树木原型,为构建林木数字孪生环境和林业资源管理提供参考。展开更多
文摘In this paper, a method and algorithm of skeleton extraction based on binary mathematical morphology is presented. Sequential structuring elements (SEs) is also studied, which is the key problem of skeleton extraction. The examples of boiler flame image processing show that the detected skeletons can present the geometric shape of flame images well.
基金This work was financially supported by the National Major Science and Technology Project(Innovation 2030)of China(Grant No.2021ZD0113701).
文摘The accurate identification of various postures in the daily life of piglets that are directly reflected by their skeleton morphology is necessary to study the behavioral characteristics of pigs.Accordingly,this study proposed a novel approach for the skeleton extraction and pose estimation of piglets.First,an improved Zhang-Suen(ZS)thinning algorithm based on morphology was used to establish the chain code mechanism of the burr and the redundant information deletion templates to achieve a single-pixel width extraction of pig skeletons.Then,body nodes were extracted on the basis of the improved DeepLabCut(DLC)algorithm,and a part affinity field(PAF)was added to realize the connection of body nodes,and consequently,construct a database of pig behavior and postures.Finally,a support vector machine was used for pose matching to recognize the main behavior of piglets.In this study,14000 images of piglets with different types of behavior were used in posture recognition experiments.Results showed that the improved algorithm based on ZS-DLC-PAF achieved the best thinning rate compared with those of distance transformation,medial axis transformation,morphology refinement,and the traditional ZS algorithm.The node tracking accuracy reached 85.08%,and the pressure test could accurately detect up to 35 nodes of 5 pigs.The average accuracy of posture matching was 89.60%.This study not only realized the single-pixel extraction of piglets’skeletons but also the connection among the different behavior body nodes of individual sows and multiple piglets.Furthermore,this study established a database of pig posture behavior,which provides a reference for studying animal behavior identification and classification and anomaly detection.
文摘针对树木三维重构过程中面临的处理速度慢、重构精度低等问题,提出一种采用激光点云数据的单木骨架三维重构方法。首先,根据点云数据类型确定组合滤波方式,以去除离群点和地面点;其次,采用一种基于内部形态描述子(ISS)和相干点漂移算法(CPD)的混合配准算法(Intrinsic Shape-Coherent Point Drift,IS-CPD),以获取单棵树木的完整点云数据;最后,采用Laplace收缩点集和拓扑细化相结合的方法提取骨架,并通过柱体构建枝干模型,实现骨架三维重构。试验结果表明,相比传统CPD算法,研究设计的配准方案精度和执行速度分别提高50%和95.8%,最终重构误差不超过2.48%。研究结果证明可有效地重构单棵树木的三维骨架,效果接近树木原型,为构建林木数字孪生环境和林业资源管理提供参考。
