Background In this work,we focus on the label layout problem:specifying the positions of overlaid virtual annotations in Virtual/Augmented Reality scenarios.Methods Designing a layout of labels that does not violate d...Background In this work,we focus on the label layout problem:specifying the positions of overlaid virtual annotations in Virtual/Augmented Reality scenarios.Methods Designing a layout of labels that does not violate domain-specific design requirements,while at the same time satisfying aesthetic and functional principles of good design,can be a daunting task even for skilled visual designers.Presenting the annotations in 3D object space instead of projection space,allows for the preservation of spatial and depth cues.This results in stable layouts in dynamic environments,since the annotations are anchored in 3D space.Results In this paper we make two major contributions.First,we propose a technique for managing the layout and rendering of annotations in Virtual/Augmented Reality scenarios by manipulating the annotations directly in 3D space.For this,we make use of Artificial Potential Fields and use 3D geometric constraints to adapt them in 3D space.Second,we introduce PartLabeling:an open source platform in the form of a web application that acts as a much-needed generic framework allowing to easily add labeling algorithms and 3D models.This serves as a catalyst for researchers in this field to make their algorithms and implementations publicly available,as well as ensure research reproducibility.The PartLabeling framework relies on a dataset that we generate as a subset of the original PartNet dataset consisting of models suitable for the label management task.The dataset consists of 10003D models with part annotations.展开更多
The paper designs a peripheral maximum gray differ-ence(PMGD)image segmentation method,a connected-compo-nent labeling(CCL)algorithm based on dynamic run length(DRL),and a real-time implementation streaming processor ...The paper designs a peripheral maximum gray differ-ence(PMGD)image segmentation method,a connected-compo-nent labeling(CCL)algorithm based on dynamic run length(DRL),and a real-time implementation streaming processor for DRL-CCL.And it verifies the function and performance in space target monitoring scene by the carrying experiment of Tianzhou-3 cargo spacecraft(TZ-3).The PMGD image segmentation method can segment the image into highly discrete and simple point tar-gets quickly,which reduces the generation of equivalences greatly and improves the real-time performance for DRL-CCL.Through parallel pipeline design,the storage of the streaming processor is optimized by 55%with no need for external me-mory,the logic is optimized by 60%,and the energy efficiency ratio is 12 times than that of the graphics processing unit,62 times than that of the digital signal proccessing,and 147 times than that of personal computers.Analyzing the results of 8756 images completed on-orbit,the speed is up to 5.88 FPS and the target detection rate is 100%.Our algorithm and implementation method meet the requirements of lightweight,high real-time,strong robustness,full-time,and stable operation in space irradia-tion environment.展开更多
Complementary-label learning(CLL)aims at finding a classifier via samples with complementary labels.Such data is considered to contain less information than ordinary-label samples.The transition matrix between the tru...Complementary-label learning(CLL)aims at finding a classifier via samples with complementary labels.Such data is considered to contain less information than ordinary-label samples.The transition matrix between the true label and the complementary label,and some loss functions have been developed to handle this problem.In this paper,we show that CLL can be transformed into ordinary classification under some mild conditions,which indicates that the complementary labels can supply enough information in most cases.As an example,an extensive misclassification error analysis was performed for the Kernel Ridge Regression(KRR)method applied to multiple complementary-label learning(MCLL),which demonstrates its superior performance compared to existing approaches.展开更多
提出了多标记分类和标记相关性的联合学习(JMLLC),在JMLLC中,构建了基于类别标记变量的有向条件依赖网络,这样不仅使得标记分类器之间可以联合学习,从而增强各个标记分类器的学习效果,而且标记分类器和标记相关性可以联合学习,从而使得...提出了多标记分类和标记相关性的联合学习(JMLLC),在JMLLC中,构建了基于类别标记变量的有向条件依赖网络,这样不仅使得标记分类器之间可以联合学习,从而增强各个标记分类器的学习效果,而且标记分类器和标记相关性可以联合学习,从而使得学习得到的标记相关性更为准确.通过采用两种不同的损失函数:logistic回归和最小二乘,分别提出了JMLLC-LR(JMLLC with logistic regression)和JMLLC-LS(JMLLC with least squares),并都拓展到再生核希尔伯特空间中.最后采用交替求解的方法求解JMLLC-LR和JMLLC-LS.在20个基准数据集上基于5种不同的评价准则的实验结果表明,JMLLC优于已提出的多标记学习算法.展开更多
文摘Background In this work,we focus on the label layout problem:specifying the positions of overlaid virtual annotations in Virtual/Augmented Reality scenarios.Methods Designing a layout of labels that does not violate domain-specific design requirements,while at the same time satisfying aesthetic and functional principles of good design,can be a daunting task even for skilled visual designers.Presenting the annotations in 3D object space instead of projection space,allows for the preservation of spatial and depth cues.This results in stable layouts in dynamic environments,since the annotations are anchored in 3D space.Results In this paper we make two major contributions.First,we propose a technique for managing the layout and rendering of annotations in Virtual/Augmented Reality scenarios by manipulating the annotations directly in 3D space.For this,we make use of Artificial Potential Fields and use 3D geometric constraints to adapt them in 3D space.Second,we introduce PartLabeling:an open source platform in the form of a web application that acts as a much-needed generic framework allowing to easily add labeling algorithms and 3D models.This serves as a catalyst for researchers in this field to make their algorithms and implementations publicly available,as well as ensure research reproducibility.The PartLabeling framework relies on a dataset that we generate as a subset of the original PartNet dataset consisting of models suitable for the label management task.The dataset consists of 10003D models with part annotations.
文摘The paper designs a peripheral maximum gray differ-ence(PMGD)image segmentation method,a connected-compo-nent labeling(CCL)algorithm based on dynamic run length(DRL),and a real-time implementation streaming processor for DRL-CCL.And it verifies the function and performance in space target monitoring scene by the carrying experiment of Tianzhou-3 cargo spacecraft(TZ-3).The PMGD image segmentation method can segment the image into highly discrete and simple point tar-gets quickly,which reduces the generation of equivalences greatly and improves the real-time performance for DRL-CCL.Through parallel pipeline design,the storage of the streaming processor is optimized by 55%with no need for external me-mory,the logic is optimized by 60%,and the energy efficiency ratio is 12 times than that of the graphics processing unit,62 times than that of the digital signal proccessing,and 147 times than that of personal computers.Analyzing the results of 8756 images completed on-orbit,the speed is up to 5.88 FPS and the target detection rate is 100%.Our algorithm and implementation method meet the requirements of lightweight,high real-time,strong robustness,full-time,and stable operation in space irradia-tion environment.
基金Supported by the Indigenous Innovation’s Capability Development Program of Huizhou University(HZU202003,HZU202020)Natural Science Foundation of Guangdong Province(2022A1515011463)+2 种基金the Project of Educational Commission of Guangdong Province(2023ZDZX1025)National Natural Science Foundation of China(12271473)Guangdong Province’s 2023 Education Science Planning Project(Higher Education Special Project)(2023GXJK505)。
文摘Complementary-label learning(CLL)aims at finding a classifier via samples with complementary labels.Such data is considered to contain less information than ordinary-label samples.The transition matrix between the true label and the complementary label,and some loss functions have been developed to handle this problem.In this paper,we show that CLL can be transformed into ordinary classification under some mild conditions,which indicates that the complementary labels can supply enough information in most cases.As an example,an extensive misclassification error analysis was performed for the Kernel Ridge Regression(KRR)method applied to multiple complementary-label learning(MCLL),which demonstrates its superior performance compared to existing approaches.
文摘提出了多标记分类和标记相关性的联合学习(JMLLC),在JMLLC中,构建了基于类别标记变量的有向条件依赖网络,这样不仅使得标记分类器之间可以联合学习,从而增强各个标记分类器的学习效果,而且标记分类器和标记相关性可以联合学习,从而使得学习得到的标记相关性更为准确.通过采用两种不同的损失函数:logistic回归和最小二乘,分别提出了JMLLC-LR(JMLLC with logistic regression)和JMLLC-LS(JMLLC with least squares),并都拓展到再生核希尔伯特空间中.最后采用交替求解的方法求解JMLLC-LR和JMLLC-LS.在20个基准数据集上基于5种不同的评价准则的实验结果表明,JMLLC优于已提出的多标记学习算法.
