Three-dimensional tree modeling is crucial for forest ecological applications.However,building accurate indi-vidual tree models still faces unresolved challenges,such as wrongly connected branches within the canopy an...Three-dimensional tree modeling is crucial for forest ecological applications.However,building accurate indi-vidual tree models still faces unresolved challenges,such as wrongly connected branches within the canopy and poor quality modeling results when dealing with tree points containing data gaps.To address these issues,this paper proposes an in-novation method for individual tree modeling based on skeleton graph optimization and fractal self-similarity.In this paper,the skeleton points are initially extracted through the Laplacian-based contraction and the farthest distance spherical sampling.To centralize the extracted skeleton points within each point set,a method for skeleton points adjusting and optimization is presented,which helps achieve centralized skeleton points,particularly in cases with incomplete branch points.Additionally,instead of using Euclidean distance or its square as edge weight,the paper proposes a novel edge weight definition,which ensures the construction of correctly connected skeleton lines,especially for branches within the canopy.To improve fidelity and robustness against outliers,fractal self-similarity is first applied in this paper to refine individual tree models and achieve better modeling results.The effectiveness of the pro-posed method is evaluated using 29 individual trees of different structure characteristics with known harvest volumes.Experimental results demonstrate that this method achieves tree volumes closest to the referenced values,with a relative mean de-viation of 0.01%and a relative root mean square error of 0.09%.Moreover,the concordance correlation co-efficient of the proposed method is 0.994,outperforming two classical individual tree modeling methods,TreeQSM(Quantitative Structure Model)and AdQSM,based on five accuracy indicators.展开更多
As point cloud of one whole vehicle body has the traits of large geometric dimension,huge data and rigorous reverse precision,one pretreatment algorithm on automobile body point cloud is put forward.The basic idea of ...As point cloud of one whole vehicle body has the traits of large geometric dimension,huge data and rigorous reverse precision,one pretreatment algorithm on automobile body point cloud is put forward.The basic idea of the registration algorithm based on the skeleton points is to construct the skeleton points of the whole vehicle model and the mark points of the separate point cloud,to search the mapped relationship between skeleton points and mark points using congruence triangle method and to match the whole vehicle point cloud using the improved iterative closed point(ICP)algorithm.The data reduction algorithm,based on average square root of distance,condenses data by three steps,computing datasets'average square root of distance in sampling cube grid,sorting order according to the value computed from the first step,choosing sampling percentage.The accuracy of the two algorithms above is proved by a registration and reduction example of whole vehicle point cloud of a certain light truck.展开更多
Human Interaction Recognition(HIR)was one of the challenging issues in computer vision research due to the involvement of multiple individuals and their mutual interactions within video frames generated from their mov...Human Interaction Recognition(HIR)was one of the challenging issues in computer vision research due to the involvement of multiple individuals and their mutual interactions within video frames generated from their movements.HIR requires more sophisticated analysis than Human Action Recognition(HAR)since HAR focuses solely on individual activities like walking or running,while HIR involves the interactions between people.This research aims to develop a robust system for recognizing five common human interactions,such as hugging,kicking,pushing,pointing,and no interaction,from video sequences using multiple cameras.In this study,a hybrid Deep Learning(DL)and Machine Learning(ML)model was employed to improve classification accuracy and generalizability.The dataset was collected in an indoor environment with four-channel cameras capturing the five types of interactions among 13 participants.The data was processed using a DL model with a fine-tuned ResNet(Residual Networks)architecture based on 2D Convolutional Neural Network(CNN)layers for feature extraction.Subsequently,machine learning models were trained and utilized for interaction classification using six commonly used ML algorithms,including SVM,KNN,RF,DT,NB,and XGBoost.The results demonstrate a high accuracy of 95.45%in classifying human interactions.The hybrid approach enabled effective learning,resulting in highly accurate performance across different interaction types.Future work will explore more complex scenarios involving multiple individuals based on the application of this architecture.展开更多
基金This work was supported by the Funding of National Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing(2024QZ-TD-26)Outstanding Young Talents Funding of Jiangxi Province(20232ACB213017)+1 种基金National Natural Science Foundation of China(NSF)(42161060,41801325)the Natural Science Foundation of Jiangxi Province(20242BAB25176,20192BAB217010)for their financial support.
文摘Three-dimensional tree modeling is crucial for forest ecological applications.However,building accurate indi-vidual tree models still faces unresolved challenges,such as wrongly connected branches within the canopy and poor quality modeling results when dealing with tree points containing data gaps.To address these issues,this paper proposes an in-novation method for individual tree modeling based on skeleton graph optimization and fractal self-similarity.In this paper,the skeleton points are initially extracted through the Laplacian-based contraction and the farthest distance spherical sampling.To centralize the extracted skeleton points within each point set,a method for skeleton points adjusting and optimization is presented,which helps achieve centralized skeleton points,particularly in cases with incomplete branch points.Additionally,instead of using Euclidean distance or its square as edge weight,the paper proposes a novel edge weight definition,which ensures the construction of correctly connected skeleton lines,especially for branches within the canopy.To improve fidelity and robustness against outliers,fractal self-similarity is first applied in this paper to refine individual tree models and achieve better modeling results.The effectiveness of the pro-posed method is evaluated using 29 individual trees of different structure characteristics with known harvest volumes.Experimental results demonstrate that this method achieves tree volumes closest to the referenced values,with a relative mean de-viation of 0.01%and a relative root mean square error of 0.09%.Moreover,the concordance correlation co-efficient of the proposed method is 0.994,outperforming two classical individual tree modeling methods,TreeQSM(Quantitative Structure Model)and AdQSM,based on five accuracy indicators.
基金This project is supported by Provincial Technology Cooperation Program of Yunnan,China(No.2003EAAAA00D043).
文摘As point cloud of one whole vehicle body has the traits of large geometric dimension,huge data and rigorous reverse precision,one pretreatment algorithm on automobile body point cloud is put forward.The basic idea of the registration algorithm based on the skeleton points is to construct the skeleton points of the whole vehicle model and the mark points of the separate point cloud,to search the mapped relationship between skeleton points and mark points using congruence triangle method and to match the whole vehicle point cloud using the improved iterative closed point(ICP)algorithm.The data reduction algorithm,based on average square root of distance,condenses data by three steps,computing datasets'average square root of distance in sampling cube grid,sorting order according to the value computed from the first step,choosing sampling percentage.The accuracy of the two algorithms above is proved by a registration and reduction example of whole vehicle point cloud of a certain light truck.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2023-00218176)and the Soonchunhyang University Research Fund.
文摘Human Interaction Recognition(HIR)was one of the challenging issues in computer vision research due to the involvement of multiple individuals and their mutual interactions within video frames generated from their movements.HIR requires more sophisticated analysis than Human Action Recognition(HAR)since HAR focuses solely on individual activities like walking or running,while HIR involves the interactions between people.This research aims to develop a robust system for recognizing five common human interactions,such as hugging,kicking,pushing,pointing,and no interaction,from video sequences using multiple cameras.In this study,a hybrid Deep Learning(DL)and Machine Learning(ML)model was employed to improve classification accuracy and generalizability.The dataset was collected in an indoor environment with four-channel cameras capturing the five types of interactions among 13 participants.The data was processed using a DL model with a fine-tuned ResNet(Residual Networks)architecture based on 2D Convolutional Neural Network(CNN)layers for feature extraction.Subsequently,machine learning models were trained and utilized for interaction classification using six commonly used ML algorithms,including SVM,KNN,RF,DT,NB,and XGBoost.The results demonstrate a high accuracy of 95.45%in classifying human interactions.The hybrid approach enabled effective learning,resulting in highly accurate performance across different interaction types.Future work will explore more complex scenarios involving multiple individuals based on the application of this architecture.
