A decision map contains complete and clear information about the image to be fused, which is crucial to various image fusion issues, especially multi-focus image fusion. However, in order to get a satisfactory image f...A decision map contains complete and clear information about the image to be fused, which is crucial to various image fusion issues, especially multi-focus image fusion. However, in order to get a satisfactory image fusion effect, getting a decision map is very necessary and usually difficult to finish. In this letter, we address this problem with convolutional neural network(CNN), aiming to get a state-of-the-art decision map. The main idea is that the max-pooling of CNN is replaced by a convolution layer, the residuals are propagated backwards by gradient descent, and the training parameters of the individual layers of the CNN are updated layer by layer. Based on this, we propose a new all CNN(ACNN)-based multi-focus image fusion method in spatial domain. We demonstrate that the decision map obtained from the ACNN is reliable and can lead to high-quality fusion results. Experimental results clearly validate that the proposed algorithm can obtain state-of-the-art fusion performance in terms of both qualitative and quantitative evaluations.展开更多
Considering the continuous advancement in the field of imaging sensor, a host of other new issues have emerged. A major problem is how to find focus areas more accurately for multi-focus image fusion. The multi-focus ...Considering the continuous advancement in the field of imaging sensor, a host of other new issues have emerged. A major problem is how to find focus areas more accurately for multi-focus image fusion. The multi-focus image fusion extracts the focused information from the source images to construct a global in-focus image which includes more information than any of the source images. In this paper, a novel multi-focus image fusion based on Laplacian operator and region optimization is proposed. The evaluation of image saliency based on Laplacian operator can easily distinguish the focus region and out of focus region. And the decision map obtained by Laplacian operator processing has less the residual information than other methods. For getting precise decision map, focus area and edge optimization based on regional connectivity and edge detection have been taken. Finally, the original images are fused through the decision map. Experimental results indicate that the proposed algorithm outperforms the other series of algorithms in terms of both subjective and objective evaluations.展开更多
Two key points of pixel-level multi-focus image fusion are the clarity measure and the pixel coeffi- cients fusion rule. Along with different improvements on these two points, various fusion schemes have been proposed...Two key points of pixel-level multi-focus image fusion are the clarity measure and the pixel coeffi- cients fusion rule. Along with different improvements on these two points, various fusion schemes have been proposed in literatures. However, the traditional clarity measures are not designed for compressive imaging measurements which are maps of source sense with random or likely ran- dom measurements matrix. This paper presents a novel efficient multi-focus image fusion frame- work for compressive imaging sensor network. Here the clarity measure of the raw compressive measurements is not obtained from the random sampling data itself but from the selected Hada- mard coefficients which can also be acquired from compressive imaging system efficiently. Then, the compressive measurements with different images are fused by selecting fusion rule. Finally, the block-based CS which coupled with iterative projection-based reconstruction is used to re- cover the fused image. Experimental results on common used testing data demonstrate the effectiveness of the proposed method.展开更多
Based on the contextual adaptation perspective of Verschueren’s Adaptation Theory,this paper explores the Chinese translation strategies of Japanese quotation sentences in the Yang translation of The Courage of One f...Based on the contextual adaptation perspective of Verschueren’s Adaptation Theory,this paper explores the Chinese translation strategies of Japanese quotation sentences in the Yang translation of The Courage of One from the perspectives of communicative context and linguistic context.The study finds that the Chinese translation of Japanese quotation sentences involves various strategies,including retaining direct quotations,converting direct quotations into statements,transforming direct quotations into attributive+noun forms,and alternating between direct and indirect quotations.This research provides a new perspective for the Chinese translation of Japanese quotation sentences and offers theoretical support for translation practices in cross-cultural communication.展开更多
In recent years,the rapid integration of artificial intelligence(AI)with various industries has led to an intelligent transformation in people’s learning and working patterns.In the field of higher vocational public ...In recent years,the rapid integration of artificial intelligence(AI)with various industries has led to an intelligent transformation in people’s learning and working patterns.In the field of higher vocational public music course teaching,AI provides intelligent teaching tools and learning platforms,while offering students timely and scientific support and companionship,enabling them to complete learning tasks more efficiently.How to explore the transformation path of teaching modes for higher vocational public music courses in the AI context has become a key consideration for frontline teachers.Based on this,this paper first analyzes the significance of teaching higher vocational public music courses in the AI context,and then proposes feasible transformation paths for teaching modes in combination with course characteristics for reference.展开更多
Background Three-dimensional(3D)shape representation using mesh data is essential in various applications,such as virtual reality and simulation technologies.Current methods for extracting features from mesh edges or ...Background Three-dimensional(3D)shape representation using mesh data is essential in various applications,such as virtual reality and simulation technologies.Current methods for extracting features from mesh edges or faces struggle with complex 3D models because edge-based approaches miss global contexts and face-based methods overlook variations in adjacent areas,which affects the overall precision.To address these issues,we propose the Feature Discrimination and Context Propagation Network(FDCPNet),which is a novel approach that synergistically integrates local and global features in mesh datasets.Methods FDCPNet is composed of two modules:(1)the Feature Discrimination Module,which employs an attention mechanism to enhance the identification of key local features,and(2)the Context Propagation Module,which enriches key local features by integrating global contextual information,thereby facilitating a more detailed and comprehensive representation of crucial areas within the mesh model.Results Experiments on popular datasets validated the effectiveness of FDCPNet,showing an improvement in the classification accuracy over the baseline MeshNet.Furthermore,even with reduced mesh face numbers and limited training data,FDCPNet achieved promising results,demonstrating its robustness in scenarios of variable complexity.展开更多
Conventional model transfer techniques,requiring the labelled source data,are not applicable in the privacy-protected medical fields.For the challenging scenarios,recent source data-free domain adaptation(SFDA)has bec...Conventional model transfer techniques,requiring the labelled source data,are not applicable in the privacy-protected medical fields.For the challenging scenarios,recent source data-free domain adaptation(SFDA)has become a mainstream solution but losing focus on the inter-sample class information.This paper proposes a new Credible Local Context Representation approach for SFDA.Our main idea is to exploit the credible local context for more discriminative representation.Specifically,we enhance the source model's discrimination by information regulating.To capture the context,a discovery method is developed that performs fixed steps walking in deep space and takes the credible features in this path as the context.In the epoch-wise adaptation,deep clustering-like training is conducted with two major updates.First,the context for all target data is constructed and then the context-fused pseudo-labels providing semantic guidance are generated.Second,for each target data,a weighting fusion on its context forms the anchored neighbourhood structure;thus,the deep clustering is switched from individual-based to coarse-grained.Also,a new regularisation building is developed on the anchored neighbourhood to drive the deep coarse-grained learning.Experiments on three benchmarks indicate that the proposed method can achieve stateof-the-art results.展开更多
Digital rural governance is a micro-level governance practice within the broader framework of building a Digital China.It involves the integration of digital technology into rural governance to drive the digital trans...Digital rural governance is a micro-level governance practice within the broader framework of building a Digital China.It involves the integration of digital technology into rural governance to drive the digital transformation of rural governance.In recognition of the varied development stages of digital rural governance,the concept of“digital context”provides an analytical lens for exploring the differences in practical models of digital rural governance.By examining the contextual characteristics and differential mechanisms of digital rural governance,this paper delves into its social foundation and technological adaptation.The research finds that the context of digital rural governance primarily encompasses three dimensions:contextual foundation,contextual logic,and contextual optimization.First,the contextual foundation of digital rural governance manifests as the social basis,comprising the social structure of villages,the type of village development,and the age structure of villagers,which constitute the social stratification forms underlying digital rural governance.Second,the contextual logic of digital rural governance focuses on the adaptation of digital technology to rural governance,promoting the adaptation of digital technology to the rural governance foundation,village governance scenarios,and villagers’digital capabilities.Third,the contextual optimization of digital rural governance emphasizes integrating digital technology with both administrative and livelihood-oriented governance affairs at the village level.This approach leverages the governance value and functional potential of digital technology to streamline digital governance processes and enhance digital governance capabilities.As a developmental direction for the transformation of rural governance,digital rural governance must not only highlight the governance advantages of digital technology but also prioritize the inherent context of rural governance.It aims to enhance the effectiveness of rural governance through digital technology and advance high-quality digital village development tailored to local conditions.展开更多
Ship detection in synthetic aperture radar(SAR)image is crucial for marine surveillance and navigation.The application of detection network based on deep learning has achieved a promising result in SAR ship detection....Ship detection in synthetic aperture radar(SAR)image is crucial for marine surveillance and navigation.The application of detection network based on deep learning has achieved a promising result in SAR ship detection.However,the existing networks encounters challenges due to the complex backgrounds,diverse scales and irregular distribution of ship targets.To address these issues,this article proposes a detection algorithm that integrates global context of the images(GCF-Net).First,we construct a global feature extraction module in the backbone network of GCF-Net,which encodes features along different spatial directions.Then,we incorporate bi-directional feature pyramid network(BiFPN)in the neck network to fuse the multi-scale features selectively.Finally,we design a convolution and transformer mixed(CTM)detection head to obtain contextual information of targets and concentrate network attention on the most informative regions of the images.Experimental results demonstrate that the proposed method achieves more accurate detection of ship targets in SAR images.展开更多
Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete v...Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete vessel segmentation and poor continuity.In this study,we propose CT-MFENet to address the aforementioned issues.First,the use of context transformer(CT)allows for the integration of contextual feature information,which helps establish the connection between pixels and solve the problem of incomplete vessel continuity.Second,multi-scale dense residual networks are used instead of traditional CNN to address the issue of inadequate local feature extraction when the model encounters vessels at multiple scales.In the decoding stage,we introduce a local-global fusion module.It enhances the localization of vascular information and reduces the semantic gap between high-and low-level features.To address the class imbalance in retinal images,we propose a hybrid loss function that enhances the segmentation ability of the model for topological structures.We conducted experiments on the publicly available DRIVE,CHASEDB1,STARE,and IOSTAR datasets.The experimental results show that our CT-MFENet performs better than most existing methods,including the baseline U-Net.展开更多
With the development of modern educational concepts and technologies,corporate financial audit is facing unprecedented challenges and opportunities.This paper first analyzes the new characteristics of corporate financ...With the development of modern educational concepts and technologies,corporate financial audit is facing unprecedented challenges and opportunities.This paper first analyzes the new characteristics of corporate financial audit in the context of modern education,including the widespread application of digital audit tools,the diversification of audit content,and the increased requirements for audit efficiency.Then,it explores the innovative practices in corporate financial audit,such as the introduction of big data analysis technology,the construction of intelligent audit platforms,and the implementation of continuous audit.The paper also conducts an in-depth study on the impact of these innovative practices on the processes,quality,and risk management of corporate financial audit.Finally,it summarizes the effectiveness of the innovation and practice of corporate financial audit in the context of modern education,and looks forward to future development trends,providing references for theoretical research and practical operations in related fields.展开更多
Unmanned aerial vehicle laser scanning(ULS)and terrestrial laser scanning(TLS)systems are effective ways to capture forest structures from top and side views,respectively.The registration of TLS and ULS data is a prer...Unmanned aerial vehicle laser scanning(ULS)and terrestrial laser scanning(TLS)systems are effective ways to capture forest structures from top and side views,respectively.The registration of TLS and ULS data is a prerequisite for a comprehensive forest structure representation.Conventional registration methods based on geometric features(e.g.,points,lines,and planes)are likely to fail due to the irregular natural point distributions of forest point clouds.Currently,automatic registration methods for forest point clouds typically rely on tree attributes(such as tree position and stem diameter).However,these methods are often unsuitable for forests with diverse compositions,complex terrains,irregular tree layouts,and insufficient common trees.In this study,an automated method is proposed to register ULS and TLS forest point clouds using ground points as registration primitives,which operates independently of tree attribute extraction and is estimated to reduce processing time by over 50%.A new evaluation method for registration accuracy evaluation is proposed,where transformation parameters from each TLS scan to the ULS obtained by the proposed registration algorithm are used to derive transformation parameters between TLS scans,which are then compared to reference parameters obtained using artificial spherical targets.Conventional ULS-TLS registration evaluation methods mostly rely on the manual corresponding points selection that is subject to inherent subjective errors,or control points in both TLS and ULS data that are difficult to collect.The proposed method presents an objective and accurate solution for ULS-TLS registration accuracy evaluation that effectively eliminates these limitations.The proposed method was tested on 12 plots with diverse stem densities,tree species,and altitudes located in a mountain forest.A total of 124 TLS scans were successfully registered to ULS data.The registration accuracy was assessed using both the conventional evaluation method and the proposed new evaluation method,with average rotation errors of 2.03 and 2.06 mrad,and average translation errors of 7.63 and 6.51 cm,respectively.The registration accuracies demonstrate that the proposed algorithm effectively and accurately registers TLS to ULS point clouds.展开更多
基金supported by the National Natural Science Foundation of China(No.61174193)
文摘A decision map contains complete and clear information about the image to be fused, which is crucial to various image fusion issues, especially multi-focus image fusion. However, in order to get a satisfactory image fusion effect, getting a decision map is very necessary and usually difficult to finish. In this letter, we address this problem with convolutional neural network(CNN), aiming to get a state-of-the-art decision map. The main idea is that the max-pooling of CNN is replaced by a convolution layer, the residuals are propagated backwards by gradient descent, and the training parameters of the individual layers of the CNN are updated layer by layer. Based on this, we propose a new all CNN(ACNN)-based multi-focus image fusion method in spatial domain. We demonstrate that the decision map obtained from the ACNN is reliable and can lead to high-quality fusion results. Experimental results clearly validate that the proposed algorithm can obtain state-of-the-art fusion performance in terms of both qualitative and quantitative evaluations.
文摘Considering the continuous advancement in the field of imaging sensor, a host of other new issues have emerged. A major problem is how to find focus areas more accurately for multi-focus image fusion. The multi-focus image fusion extracts the focused information from the source images to construct a global in-focus image which includes more information than any of the source images. In this paper, a novel multi-focus image fusion based on Laplacian operator and region optimization is proposed. The evaluation of image saliency based on Laplacian operator can easily distinguish the focus region and out of focus region. And the decision map obtained by Laplacian operator processing has less the residual information than other methods. For getting precise decision map, focus area and edge optimization based on regional connectivity and edge detection have been taken. Finally, the original images are fused through the decision map. Experimental results indicate that the proposed algorithm outperforms the other series of algorithms in terms of both subjective and objective evaluations.
文摘Two key points of pixel-level multi-focus image fusion are the clarity measure and the pixel coeffi- cients fusion rule. Along with different improvements on these two points, various fusion schemes have been proposed in literatures. However, the traditional clarity measures are not designed for compressive imaging measurements which are maps of source sense with random or likely ran- dom measurements matrix. This paper presents a novel efficient multi-focus image fusion frame- work for compressive imaging sensor network. Here the clarity measure of the raw compressive measurements is not obtained from the random sampling data itself but from the selected Hada- mard coefficients which can also be acquired from compressive imaging system efficiently. Then, the compressive measurements with different images are fused by selecting fusion rule. Finally, the block-based CS which coupled with iterative projection-based reconstruction is used to re- cover the fused image. Experimental results on common used testing data demonstrate the effectiveness of the proposed method.
文摘Based on the contextual adaptation perspective of Verschueren’s Adaptation Theory,this paper explores the Chinese translation strategies of Japanese quotation sentences in the Yang translation of The Courage of One from the perspectives of communicative context and linguistic context.The study finds that the Chinese translation of Japanese quotation sentences involves various strategies,including retaining direct quotations,converting direct quotations into statements,transforming direct quotations into attributive+noun forms,and alternating between direct and indirect quotations.This research provides a new perspective for the Chinese translation of Japanese quotation sentences and offers theoretical support for translation practices in cross-cultural communication.
文摘In recent years,the rapid integration of artificial intelligence(AI)with various industries has led to an intelligent transformation in people’s learning and working patterns.In the field of higher vocational public music course teaching,AI provides intelligent teaching tools and learning platforms,while offering students timely and scientific support and companionship,enabling them to complete learning tasks more efficiently.How to explore the transformation path of teaching modes for higher vocational public music courses in the AI context has become a key consideration for frontline teachers.Based on this,this paper first analyzes the significance of teaching higher vocational public music courses in the AI context,and then proposes feasible transformation paths for teaching modes in combination with course characteristics for reference.
基金Supported by the National Key R&D Program of China(2022YFC3803600).
文摘Background Three-dimensional(3D)shape representation using mesh data is essential in various applications,such as virtual reality and simulation technologies.Current methods for extracting features from mesh edges or faces struggle with complex 3D models because edge-based approaches miss global contexts and face-based methods overlook variations in adjacent areas,which affects the overall precision.To address these issues,we propose the Feature Discrimination and Context Propagation Network(FDCPNet),which is a novel approach that synergistically integrates local and global features in mesh datasets.Methods FDCPNet is composed of two modules:(1)the Feature Discrimination Module,which employs an attention mechanism to enhance the identification of key local features,and(2)the Context Propagation Module,which enriches key local features by integrating global contextual information,thereby facilitating a more detailed and comprehensive representation of crucial areas within the mesh model.Results Experiments on popular datasets validated the effectiveness of FDCPNet,showing an improvement in the classification accuracy over the baseline MeshNet.Furthermore,even with reduced mesh face numbers and limited training data,FDCPNet achieved promising results,demonstrating its robustness in scenarios of variable complexity.
基金National Key R&D Program of China,Grant/Award Numbers:2018YFE0203900,2020YFB1313600German Research Foundation,Hamburg Landesforschungsförderungsprojekt Cross,Grant/Award Number:Sonderforschungsbereich Transregio 169+2 种基金Shanghai Artificial Intelligence Innovation Development Special Support Project,Grant/Award Number:3920365001Horizon2020 RISE project STEP2DYNA,Grant/Award Number:691154National Natural Science Foundation of China,Grant/Award Numbers:61773083,62206168,62276048,U1813202。
文摘Conventional model transfer techniques,requiring the labelled source data,are not applicable in the privacy-protected medical fields.For the challenging scenarios,recent source data-free domain adaptation(SFDA)has become a mainstream solution but losing focus on the inter-sample class information.This paper proposes a new Credible Local Context Representation approach for SFDA.Our main idea is to exploit the credible local context for more discriminative representation.Specifically,we enhance the source model's discrimination by information regulating.To capture the context,a discovery method is developed that performs fixed steps walking in deep space and takes the credible features in this path as the context.In the epoch-wise adaptation,deep clustering-like training is conducted with two major updates.First,the context for all target data is constructed and then the context-fused pseudo-labels providing semantic guidance are generated.Second,for each target data,a weighting fusion on its context forms the anchored neighbourhood structure;thus,the deep clustering is switched from individual-based to coarse-grained.Also,a new regularisation building is developed on the anchored neighbourhood to drive the deep coarse-grained learning.Experiments on three benchmarks indicate that the proposed method can achieve stateof-the-art results.
基金funded by the“Digital Governance Transformation in Rural Spatial Changes”(ID:22CSH015)a Youth Project under the National Social Science Fund of China.
文摘Digital rural governance is a micro-level governance practice within the broader framework of building a Digital China.It involves the integration of digital technology into rural governance to drive the digital transformation of rural governance.In recognition of the varied development stages of digital rural governance,the concept of“digital context”provides an analytical lens for exploring the differences in practical models of digital rural governance.By examining the contextual characteristics and differential mechanisms of digital rural governance,this paper delves into its social foundation and technological adaptation.The research finds that the context of digital rural governance primarily encompasses three dimensions:contextual foundation,contextual logic,and contextual optimization.First,the contextual foundation of digital rural governance manifests as the social basis,comprising the social structure of villages,the type of village development,and the age structure of villagers,which constitute the social stratification forms underlying digital rural governance.Second,the contextual logic of digital rural governance focuses on the adaptation of digital technology to rural governance,promoting the adaptation of digital technology to the rural governance foundation,village governance scenarios,and villagers’digital capabilities.Third,the contextual optimization of digital rural governance emphasizes integrating digital technology with both administrative and livelihood-oriented governance affairs at the village level.This approach leverages the governance value and functional potential of digital technology to streamline digital governance processes and enhance digital governance capabilities.As a developmental direction for the transformation of rural governance,digital rural governance must not only highlight the governance advantages of digital technology but also prioritize the inherent context of rural governance.It aims to enhance the effectiveness of rural governance through digital technology and advance high-quality digital village development tailored to local conditions.
基金supported by the National Science Fund for Distinguished Young Scholars(No.62325104).
文摘Ship detection in synthetic aperture radar(SAR)image is crucial for marine surveillance and navigation.The application of detection network based on deep learning has achieved a promising result in SAR ship detection.However,the existing networks encounters challenges due to the complex backgrounds,diverse scales and irregular distribution of ship targets.To address these issues,this article proposes a detection algorithm that integrates global context of the images(GCF-Net).First,we construct a global feature extraction module in the backbone network of GCF-Net,which encodes features along different spatial directions.Then,we incorporate bi-directional feature pyramid network(BiFPN)in the neck network to fuse the multi-scale features selectively.Finally,we design a convolution and transformer mixed(CTM)detection head to obtain contextual information of targets and concentrate network attention on the most informative regions of the images.Experimental results demonstrate that the proposed method achieves more accurate detection of ship targets in SAR images.
基金the National Natural Science Foundation of China(No.62266025)。
文摘Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete vessel segmentation and poor continuity.In this study,we propose CT-MFENet to address the aforementioned issues.First,the use of context transformer(CT)allows for the integration of contextual feature information,which helps establish the connection between pixels and solve the problem of incomplete vessel continuity.Second,multi-scale dense residual networks are used instead of traditional CNN to address the issue of inadequate local feature extraction when the model encounters vessels at multiple scales.In the decoding stage,we introduce a local-global fusion module.It enhances the localization of vascular information and reduces the semantic gap between high-and low-level features.To address the class imbalance in retinal images,we propose a hybrid loss function that enhances the segmentation ability of the model for topological structures.We conducted experiments on the publicly available DRIVE,CHASEDB1,STARE,and IOSTAR datasets.The experimental results show that our CT-MFENet performs better than most existing methods,including the baseline U-Net.
文摘With the development of modern educational concepts and technologies,corporate financial audit is facing unprecedented challenges and opportunities.This paper first analyzes the new characteristics of corporate financial audit in the context of modern education,including the widespread application of digital audit tools,the diversification of audit content,and the increased requirements for audit efficiency.Then,it explores the innovative practices in corporate financial audit,such as the introduction of big data analysis technology,the construction of intelligent audit platforms,and the implementation of continuous audit.The paper also conducts an in-depth study on the impact of these innovative practices on the processes,quality,and risk management of corporate financial audit.Finally,it summarizes the effectiveness of the innovation and practice of corporate financial audit in the context of modern education,and looks forward to future development trends,providing references for theoretical research and practical operations in related fields.
基金supported partially by the National Key Research and Development Program of China(No.2023YFF1303901)the National Natural Science Foundation of China(Nos.32171789,12411530088,and 32371654)the Joint Open Funded Project of State Key Laboratory of Geo-Information Engineering and Key Laboratory of the Ministry of Natural Resources for Surveying and Mapping Science and Geo-spatial Information Technology(No.2022-02-02).
文摘Unmanned aerial vehicle laser scanning(ULS)and terrestrial laser scanning(TLS)systems are effective ways to capture forest structures from top and side views,respectively.The registration of TLS and ULS data is a prerequisite for a comprehensive forest structure representation.Conventional registration methods based on geometric features(e.g.,points,lines,and planes)are likely to fail due to the irregular natural point distributions of forest point clouds.Currently,automatic registration methods for forest point clouds typically rely on tree attributes(such as tree position and stem diameter).However,these methods are often unsuitable for forests with diverse compositions,complex terrains,irregular tree layouts,and insufficient common trees.In this study,an automated method is proposed to register ULS and TLS forest point clouds using ground points as registration primitives,which operates independently of tree attribute extraction and is estimated to reduce processing time by over 50%.A new evaluation method for registration accuracy evaluation is proposed,where transformation parameters from each TLS scan to the ULS obtained by the proposed registration algorithm are used to derive transformation parameters between TLS scans,which are then compared to reference parameters obtained using artificial spherical targets.Conventional ULS-TLS registration evaluation methods mostly rely on the manual corresponding points selection that is subject to inherent subjective errors,or control points in both TLS and ULS data that are difficult to collect.The proposed method presents an objective and accurate solution for ULS-TLS registration accuracy evaluation that effectively eliminates these limitations.The proposed method was tested on 12 plots with diverse stem densities,tree species,and altitudes located in a mountain forest.A total of 124 TLS scans were successfully registered to ULS data.The registration accuracy was assessed using both the conventional evaluation method and the proposed new evaluation method,with average rotation errors of 2.03 and 2.06 mrad,and average translation errors of 7.63 and 6.51 cm,respectively.The registration accuracies demonstrate that the proposed algorithm effectively and accurately registers TLS to ULS point clouds.
基金SuppoSed by the National Natural Science Foundation of China under Grant Nos.6067319560703078(国家自然科学基金)+2 种基金the National High-Tech Research and Development Plan of China under Grant No.2007AA04Z113(国家高技术研究发展计划(863))the National Basic Research Program of China under Grant No.2006CB303105(国家重点基础研究发展规划(973))the National Key Technology R&D Program of China under Grant No.2006BAF01A17(国家科技支撑计划)
