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
The key pose frames of a human motion pose sequence,play an important role in the compression,retrieval and semantic analysis of continuous human motion.The current available clustering methods in literatures are diff...The key pose frames of a human motion pose sequence,play an important role in the compression,retrieval and semantic analysis of continuous human motion.The current available clustering methods in literatures are difficult to determine the number of key pose frames automatically,and may destroy the postures’ temporal relationships while extracting key frames.To deal with this problem,this paper proposes a new key pose frames extraction method on the basis of 3D space distances of joint points and the improved X-means clustering algorithm.According to the proposed extraction method,the final key pose frame sequence could be obtained by describing the posture of human body with space distance of particular joint points and then the time-constraint X-mean algorithm is applied to cluster and filtrate the posture sequence.The experimental results show that the proposed method can automatically determine the number of key frames and save the temporal characteristics of motion frames according to the motion pose sequence.展开更多
In this paper, an approach of roads network extraction from high resolution satellite images is presented. First, the approach extracts road surface from satellite image using one-class support vector machine （SVM）....In this paper, an approach of roads network extraction from high resolution satellite images is presented. First, the approach extracts road surface from satellite image using one-class support vector machine （SVM）. Second, the road topology is built from the road surface. The last output of the approach is a series of road segments which is represented by a sequence of points as well as the topological relations among them. The approach includes four steps. In the first step one-class support vector machine is used for classifying pixel of the satellite images to road class or non-road class. In the second step filling holes and connecting gaps for the SVM＇s classification result is applied through mathematical morphology close operation. In the third step the road segment is extracted by a series of operations which include skeletonization, thin, branch pruning and road segmentation. In the last step a geometrical adjustment process is applied through analyzing the road segment curvature. The experiment results demonstrate its robustness and viability on extracting road network from high resolution satellite images.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
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 Natural Science Foundation of China(61303127)Project of Science and Technology Department of Sichuan Province(2014SZ0223,2014GZ0100,2015GZ0212)+1 种基金Key Program of Education Department of Sichuan Province(11ZA130,13ZA0169)Postgraduate Innovation Fund Project by Southwest University of Science and Technology(15ycx057)
文摘The key pose frames of a human motion pose sequence,play an important role in the compression,retrieval and semantic analysis of continuous human motion.The current available clustering methods in literatures are difficult to determine the number of key pose frames automatically,and may destroy the postures’ temporal relationships while extracting key frames.To deal with this problem,this paper proposes a new key pose frames extraction method on the basis of 3D space distances of joint points and the improved X-means clustering algorithm.According to the proposed extraction method,the final key pose frame sequence could be obtained by describing the posture of human body with space distance of particular joint points and then the time-constraint X-mean algorithm is applied to cluster and filtrate the posture sequence.The experimental results show that the proposed method can automatically determine the number of key frames and save the temporal characteristics of motion frames according to the motion pose sequence.
基金Supported by National Natural Science Foundation of China(NSFC)(61232014,61421062,61472010)National Key Technology R&D Program of China(2015BAK01B06)
文摘In this paper, an approach of roads network extraction from high resolution satellite images is presented. First, the approach extracts road surface from satellite image using one-class support vector machine （SVM）. Second, the road topology is built from the road surface. The last output of the approach is a series of road segments which is represented by a sequence of points as well as the topological relations among them. The approach includes four steps. In the first step one-class support vector machine is used for classifying pixel of the satellite images to road class or non-road class. In the second step filling holes and connecting gaps for the SVM＇s classification result is applied through mathematical morphology close operation. In the third step the road segment is extracted by a series of operations which include skeletonization, thin, branch pruning and road segmentation. In the last step a geometrical adjustment process is applied through analyzing the road segment curvature. The experiment results demonstrate its robustness and viability on extracting road network from high resolution satellite images.
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.
