Studying various aurora morphology helps us understand space's physical processes and the mechanisms behind these patterns.Auroral arcs are the brightest and most prominent auroral patterns.Due to the difficulty i...Studying various aurora morphology helps us understand space's physical processes and the mechanisms behind these patterns.Auroral arcs are the brightest and most prominent auroral patterns.Due to the difficulty in precisely defining auroral shape edges,auroral arc skeleton extraction is expected as an alternative representation for studying auroral morphology,resorting skeletons extract key morphological features from complex auroral shapes.Transformer models provide a better understanding of the relationship between the overall morphology and the details when processing image data,so we proposed a Transformer-based method for auroral arc skeleton extraction.Combined with ridge-guided annotation on all-sky images,a Transformer-based skeleton extractor is trained and used to estimate the number of auroral arcs.Experiments demonstrate that the Transformer-based model can more effectively capture structural information and local details of auroral arcs,which is suitable for complex auroral morphologies.展开更多
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
Pig body measurement is an important evaluation criterion for breeding and production management.Automatic measurement algorithms for pig body sizes exhibit sensitivity to the point cloud posture,but non-standard pig ...Pig body measurement is an important evaluation criterion for breeding and production management.Automatic measurement algorithms for pig body sizes exhibit sensitivity to the point cloud posture,but non-standard pig postures may result in inaccurate joint point localization in body measurement,further affecting measurement accuracy and the commercial application of these algorithms.To address this challenge,this paper proposed a pig point cloud posture transformation method based on pig’s skeleton model to adjust non-standard postures before conducting body size measurements.The method utilized an improved L1-median skeleton model to extract the three-dimensional skeleton of the pig point cloud,capturing the skeleton joint points on the target pig’s head,body,and limbs.By binding the skeleton joint points with the local point cloud and using rotation matrices,non-standard postures were adjusted to standard ones,enabling accurate body size measurements.The experimental results demonstrated that the average relative errors between the transferred posture and the original standard posture were reduced to 0.89%in body length,0.76%in body width(front),1%in body width(back),0.89%in body height(front),1.7%in body height(back),2.03%in thoracic circumference,3.37%in abdominal circumference,and 1.89%in rump circumference.To conclude,the posture standardization transfer method can significantly reduce errors in important body size parameters such as body length,body height,and body width.The method displays a greater stability and robustness compared to existing posture normalization and regression adjustment methods,providing both guidance and insight for future research in intelligent agriculture.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
Artificial intelligence is increasingly being applied in the field of video analysis,particularly in the area of public safety where video surveillance equipment such as closed-circuit television(CCTV)is used and auto...Artificial intelligence is increasingly being applied in the field of video analysis,particularly in the area of public safety where video surveillance equipment such as closed-circuit television(CCTV)is used and automated analysis of video information is required.However,various issues such as data size limitations and low processing speeds make real-time extraction of video data challenging.Video analysis technology applies object classification,detection,and relationship analysis to continuous 2D frame data,and the various meanings within the video are thus analyzed based on the extracted basic data.Motion recognition is key in this analysis.Motion recognition is a challenging field that analyzes human body movements,requiring the interpretation of complex movements of human joints and the relationships between various objects.The deep learning-based human skeleton detection algorithm is a representative motion recognition algorithm.Recently,motion analysis models such as the SlowFast network algorithm,have also been developed with excellent performance.However,these models do not operate properly in most wide-angle video environments outdoors,displaying low response speed,as expected from motion classification extraction in environments associated with high-resolution images.The proposed method achieves high level of extraction and accuracy by improving SlowFast’s input data preprocessing and data structure methods.The input data are preprocessed through object tracking and background removal using YOLO and DeepSORT.A higher performance than that of a single model is achieved by improving the existing SlowFast’s data structure into a frame unit structure.Based on the confusion matrix,accuracies of 70.16%and 70.74%were obtained for the existing SlowFast and proposed model,respectively,indicating a 0.58%increase in accuracy.Comparing detection,based on behavioral classification,the existing SlowFast detected 2,341,164 cases,whereas the proposed model detected 3,119,323 cases,which is an increase of 33.23%.展开更多
Tree skeleton could be useful to agronomy researchers because the skeleton describes the shape and topological structure of a tree.The phenomenon of organs’mutual occlusion in fruit tree canopy is usually very seriou...Tree skeleton could be useful to agronomy researchers because the skeleton describes the shape and topological structure of a tree.The phenomenon of organs’mutual occlusion in fruit tree canopy is usually very serious,this should result in a large amount of data missing in directed laser scanning 3D point clouds from a fruit tree.However,traditional approaches can be ineffective and problematic in extracting the tree skeleton correctly when the tree point clouds contain occlusions and missing points.To overcome this limitation,we present a method for accurate and fast extracting the skeleton of fruit tree from laser scanner measured 3D point clouds.The proposed method selects the start point and endpoint of a branch from the point clouds by user’s manual interaction,then a backward searching is used to find a path from the 3D point cloud with a radius parameter as a restriction.The experimental results in several kinds of fruit trees demonstrate that our method can extract the skeleton of a leafy fruit tree with highly accuracy.展开更多
基金supported by the National Natural Science Foundation of China(Grant no.41874173)。
文摘Studying various aurora morphology helps us understand space's physical processes and the mechanisms behind these patterns.Auroral arcs are the brightest and most prominent auroral patterns.Due to the difficulty in precisely defining auroral shape edges,auroral arc skeleton extraction is expected as an alternative representation for studying auroral morphology,resorting skeletons extract key morphological features from complex auroral shapes.Transformer models provide a better understanding of the relationship between the overall morphology and the details when processing image data,so we proposed a Transformer-based method for auroral arc skeleton extraction.Combined with ridge-guided annotation on all-sky images,a Transformer-based skeleton extractor is trained and used to estimate the number of auroral arcs.Experiments demonstrate that the Transformer-based model can more effectively capture structural information and local details of auroral arcs,which is suitable for complex auroral morphologies.
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supported by the National Key R&D Program(2023YFD1300202)National Natural Science Foundation of China(Grant No.32172780)Key Laboratory of Smart Agricultural Technology in Tropical South China,National Engineering Research Center for Breeding Swine Industry,and Guangdong Engineering Technology Research Center for Agricultural Farming Internet of Things.
文摘Pig body measurement is an important evaluation criterion for breeding and production management.Automatic measurement algorithms for pig body sizes exhibit sensitivity to the point cloud posture,but non-standard pig postures may result in inaccurate joint point localization in body measurement,further affecting measurement accuracy and the commercial application of these algorithms.To address this challenge,this paper proposed a pig point cloud posture transformation method based on pig’s skeleton model to adjust non-standard postures before conducting body size measurements.The method utilized an improved L1-median skeleton model to extract the three-dimensional skeleton of the pig point cloud,capturing the skeleton joint points on the target pig’s head,body,and limbs.By binding the skeleton joint points with the local point cloud and using rotation matrices,non-standard postures were adjusted to standard ones,enabling accurate body size measurements.The experimental results demonstrated that the average relative errors between the transferred posture and the original standard posture were reduced to 0.89%in body length,0.76%in body width(front),1%in body width(back),0.89%in body height(front),1.7%in body height(back),2.03%in thoracic circumference,3.37%in abdominal circumference,and 1.89%in rump circumference.To conclude,the posture standardization transfer method can significantly reduce errors in important body size parameters such as body length,body height,and body width.The method displays a greater stability and robustness compared to existing posture normalization and regression adjustment methods,providing both guidance and insight for future research in intelligent agriculture.
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.
基金supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2020R1A6A1A03040583)supported by Kyonggi University’s Graduate Research Assistantship 2023.
文摘Artificial intelligence is increasingly being applied in the field of video analysis,particularly in the area of public safety where video surveillance equipment such as closed-circuit television(CCTV)is used and automated analysis of video information is required.However,various issues such as data size limitations and low processing speeds make real-time extraction of video data challenging.Video analysis technology applies object classification,detection,and relationship analysis to continuous 2D frame data,and the various meanings within the video are thus analyzed based on the extracted basic data.Motion recognition is key in this analysis.Motion recognition is a challenging field that analyzes human body movements,requiring the interpretation of complex movements of human joints and the relationships between various objects.The deep learning-based human skeleton detection algorithm is a representative motion recognition algorithm.Recently,motion analysis models such as the SlowFast network algorithm,have also been developed with excellent performance.However,these models do not operate properly in most wide-angle video environments outdoors,displaying low response speed,as expected from motion classification extraction in environments associated with high-resolution images.The proposed method achieves high level of extraction and accuracy by improving SlowFast’s input data preprocessing and data structure methods.The input data are preprocessed through object tracking and background removal using YOLO and DeepSORT.A higher performance than that of a single model is achieved by improving the existing SlowFast’s data structure into a frame unit structure.Based on the confusion matrix,accuracies of 70.16%and 70.74%were obtained for the existing SlowFast and proposed model,respectively,indicating a 0.58%increase in accuracy.Comparing detection,based on behavioral classification,the existing SlowFast detected 2,341,164 cases,whereas the proposed model detected 3,119,323 cases,which is an increase of 33.23%.
基金This work is supported through grants from the National Natural Science Foundation of China(No.61762013)basic ability improvement project for young and middle-aged teachers in universities of Guangxi province(No.2018KY0078)+1 种基金Science and technology program of Guangxi(No.2018AD19339)Research Fund of Guangxi Key Lab of Multi-Source Information Mining and Security(No.20-A-02-02).
文摘Tree skeleton could be useful to agronomy researchers because the skeleton describes the shape and topological structure of a tree.The phenomenon of organs’mutual occlusion in fruit tree canopy is usually very serious,this should result in a large amount of data missing in directed laser scanning 3D point clouds from a fruit tree.However,traditional approaches can be ineffective and problematic in extracting the tree skeleton correctly when the tree point clouds contain occlusions and missing points.To overcome this limitation,we present a method for accurate and fast extracting the skeleton of fruit tree from laser scanner measured 3D point clouds.The proposed method selects the start point and endpoint of a branch from the point clouds by user’s manual interaction,then a backward searching is used to find a path from the 3D point cloud with a radius parameter as a restriction.The experimental results in several kinds of fruit trees demonstrate that our method can extract the skeleton of a leafy fruit tree with highly accuracy.
