To address the issues of unknown target size,blurred edges,background interference and low contrast in infrared small target detection,this paper proposes a method based on density peaks searching and weighted multi-f...To address the issues of unknown target size,blurred edges,background interference and low contrast in infrared small target detection,this paper proposes a method based on density peaks searching and weighted multi-feature local difference.Firstly,an improved high-boost filter is used for preprocessing to eliminate background clutter and high-brightness interference,thereby increasing the probability of capturing real targets in the density peak search.Secondly,a triple-layer window is used to extract features from the area surrounding candidate targets,addressing the uncertainty of small target sizes.By calculating multi-feature local differences between the triple-layer windows,the problems of blurred target edges and low contrast are resolved.To balance the contribution of different features,intra-class distance is used to calculate weights,achieving weighted fusion of multi-feature local differences to obtain the weighted multi-feature local differences of candidate targets.The real targets are then extracted using the interquartile range.Experiments on datasets such as SIRST and IRSTD-IK show that the proposed method is suitable for various complex types and demonstrates good robustness and detection performance.展开更多
The detection of small targets poses a significant challenge for infrared search and tracking (IRST) systems,as they must strike a delicate balance between accuracy and speed.In this paper,we propose a detection algor...The detection of small targets poses a significant challenge for infrared search and tracking (IRST) systems,as they must strike a delicate balance between accuracy and speed.In this paper,we propose a detection algorithm based on spatial attention density peaks searching (SADPS) and an adaptive window selection scheme.First,the difference-of-Gaussians (DoG) filter is introduced for preprocessing raw infrared images.Second,the image is processed by SADPS.Third,an adaptive window selection scheme is applied to obtain window templates for the target scale size.Then,the small target feature is used to enhance the target and suppress the background.Finally,the true targets are segmented through a threshold.The experimental results show that compared with the seven state-of-the-art small targets detection baseline algorithms,the proposed method not only has better detection accuracy,but also has reasonable time consumption.展开更多
针对现有土石坝渗流监控指标拟定方法存在主观性较强和精度较低的不足,基于智能算法改进的超阈值(peaks over threshold,POT)模型,提出了优化的土石坝渗流监控指标拟定方法.以3σ准则为确定最优阈值的理论基础,采用基于混沌映射、结合L...针对现有土石坝渗流监控指标拟定方法存在主观性较强和精度较低的不足,基于智能算法改进的超阈值(peaks over threshold,POT)模型,提出了优化的土石坝渗流监控指标拟定方法.以3σ准则为确定最优阈值的理论基础,采用基于混沌映射、结合Levy飞行和逆向学习的动态选择策略改进的麻雀搜索算法(improved chaos sparrow search algorithm,ICSSA),对POT模型中阈值的选取方法进行优化.建立了ICSSA-POT模型,实现对监测资料尾部数据的拟合,从而得到更为合理的土石坝渗流监控指标.研究表明,相比于传统方法,所提方法可有效避免主观性与随机误差,得到的监测资料尾部数据的拟合决定系数提高了5%,具有更高的计算精度,拟定的渗流监控指标更偏于安全,对防范土石坝渗流破坏、确保土石坝安全长效运行具有较强的指导意义.展开更多
In consideration of the limitation of super-peer overlay network, the semantic information was introduced into the super-peers＇ organization. A novel P2P （peer-to-peer） searching model, SSP2P, was put forward. The ...In consideration of the limitation of super-peer overlay network, the semantic information was introduced into the super-peers＇ organization. A novel P2P （peer-to-peer） searching model, SSP2P, was put forward. The peers in the model were organized in a natural area autonomy system （AAS） based on the smallworld theory. A super-peer was selected in each AAS based on power law; and all the super-peers formed different super-peer semantic networks. Thus, a hierarchical super-peer overlay network was formed. The results show that the model reduces the communication cost and enhances the search efficiency while ensuring the system expansibility. It proves that the introduction of semantic information in the construction of a super-peer overlay is favorable to P2P system capability.展开更多
Distributed data sources which employ taxonomy hierarchy to describe the contents of their objects are considered, and a super-peer-based semantic overlay network （SSON） is proposed for sharing and searching their d...Distributed data sources which employ taxonomy hierarchy to describe the contents of their objects are considered, and a super-peer-based semantic overlay network （SSON） is proposed for sharing and searching their data objects. In SSON, peers are dynamically clustered into many semantic clusters based on the semantics of their data objects and organized in the semantic clusters into a semantic overlay network. Each semantic cluster consists of a super-peer and more peers, and is only responsible for answering queries in its semantic subspace. A query is first routed to the appropriate semantic clusters by an efficient searching algorithm, and then it is forwarded to the specific peers that hold the relevant data objects. Experimental results indicate that SSON has good scalability and achieves a competitive trade-off between search efficiency and costs.展开更多
A novel idea,called the optimal shape subspace （OSS） is first proposed for optimizing active shape model （ASM） search.It is constructed from the principal shape subspace and the principal shape variance subspace.I...A novel idea,called the optimal shape subspace （OSS） is first proposed for optimizing active shape model （ASM） search.It is constructed from the principal shape subspace and the principal shape variance subspace.It allows the reconstructed shape to vary more than that reconstructed in the standard ASM shape space,hence it is more expressive in representing shapes in real life.Then a cost function is developed,based on a study on the search process.An optimal searching method using the feedback information provided by the evaluation cost is proposed to improve the performance of ASM alignment.Experimental results show that the proposed OSS can offer the maximum shape variation with reserving the principal information and a unique local optimal shape is acquired after optimal searching.The combination of OSS and optimal searching can improve the ASM performance greatly.展开更多
For density inversion of gravity anomaly data, once the inversion method is determined, the main factors affecting the inversion result are the inversion parameters and subdivision scheme. A set of reasonable inversio...For density inversion of gravity anomaly data, once the inversion method is determined, the main factors affecting the inversion result are the inversion parameters and subdivision scheme. A set of reasonable inversion parameters and subdivision scheme can, not only improve the inversion process efficiency, but also ensure inversion result accuracy. The gravity inversion method based on correlation searching and the golden section algorithm is an effective potential field inversion method. It can be used to invert 2D and 3D physical properties with potential data observed on flat or rough surfaces. In this paper, we introduce in detail the density inversion principles based on correlation searching and the golden section algorithm. Considering that the gold section algorithm is not globally optimized. we present a heuristic method to ensure the inversion result is globally optimized. With a series of model tests, we systematically compare and analyze the inversion result efficiency and accuracy with different parameters. Based on the model test results, we conclude the selection principles for each inversion parameter with which the inversion accuracy can be obviously improved.展开更多
基金supported by the National Natural Science Foundation of China (No.52205548)。
文摘To address the issues of unknown target size,blurred edges,background interference and low contrast in infrared small target detection,this paper proposes a method based on density peaks searching and weighted multi-feature local difference.Firstly,an improved high-boost filter is used for preprocessing to eliminate background clutter and high-brightness interference,thereby increasing the probability of capturing real targets in the density peak search.Secondly,a triple-layer window is used to extract features from the area surrounding candidate targets,addressing the uncertainty of small target sizes.By calculating multi-feature local differences between the triple-layer windows,the problems of blurred target edges and low contrast are resolved.To balance the contribution of different features,intra-class distance is used to calculate weights,achieving weighted fusion of multi-feature local differences to obtain the weighted multi-feature local differences of candidate targets.The real targets are then extracted using the interquartile range.Experiments on datasets such as SIRST and IRSTD-IK show that the proposed method is suitable for various complex types and demonstrates good robustness and detection performance.
基金supported by the National 14th Five-Year Plan Preliminary Research Project (No.514010405-207)。
文摘The detection of small targets poses a significant challenge for infrared search and tracking (IRST) systems,as they must strike a delicate balance between accuracy and speed.In this paper,we propose a detection algorithm based on spatial attention density peaks searching (SADPS) and an adaptive window selection scheme.First,the difference-of-Gaussians (DoG) filter is introduced for preprocessing raw infrared images.Second,the image is processed by SADPS.Third,an adaptive window selection scheme is applied to obtain window templates for the target scale size.Then,the small target feature is used to enhance the target and suppress the background.Finally,the true targets are segmented through a threshold.The experimental results show that compared with the seven state-of-the-art small targets detection baseline algorithms,the proposed method not only has better detection accuracy,but also has reasonable time consumption.
基金The National Natural Science Foundation of China(No.60573127), Specialized Research Fund for the Doctoral Program of Higher Education (No.20040533036).
文摘In consideration of the limitation of super-peer overlay network, the semantic information was introduced into the super-peers＇ organization. A novel P2P （peer-to-peer） searching model, SSP2P, was put forward. The peers in the model were organized in a natural area autonomy system （AAS） based on the smallworld theory. A super-peer was selected in each AAS based on power law; and all the super-peers formed different super-peer semantic networks. Thus, a hierarchical super-peer overlay network was formed. The results show that the model reduces the communication cost and enhances the search efficiency while ensuring the system expansibility. It proves that the introduction of semantic information in the construction of a super-peer overlay is favorable to P2P system capability.
基金The National Natural Science Foundation of China(No60573089)the Natural Science Foundation of Liaoning Province(No20052031)the National High Technology Research and Develop-ment Program of China (863Program)(No2006AA09Z139)
文摘Distributed data sources which employ taxonomy hierarchy to describe the contents of their objects are considered, and a super-peer-based semantic overlay network （SSON） is proposed for sharing and searching their data objects. In SSON, peers are dynamically clustered into many semantic clusters based on the semantics of their data objects and organized in the semantic clusters into a semantic overlay network. Each semantic cluster consists of a super-peer and more peers, and is only responsible for answering queries in its semantic subspace. A query is first routed to the appropriate semantic clusters by an efficient searching algorithm, and then it is forwarded to the specific peers that hold the relevant data objects. Experimental results indicate that SSON has good scalability and achieves a competitive trade-off between search efficiency and costs.
基金21st Century Education Revitalization Project (No.301703201).
文摘A novel idea,called the optimal shape subspace （OSS） is first proposed for optimizing active shape model （ASM） search.It is constructed from the principal shape subspace and the principal shape variance subspace.It allows the reconstructed shape to vary more than that reconstructed in the standard ASM shape space,hence it is more expressive in representing shapes in real life.Then a cost function is developed,based on a study on the search process.An optimal searching method using the feedback information provided by the evaluation cost is proposed to improve the performance of ASM alignment.Experimental results show that the proposed OSS can offer the maximum shape variation with reserving the principal information and a unique local optimal shape is acquired after optimal searching.The combination of OSS and optimal searching can improve the ASM performance greatly.
基金supported by Specialized Research Fund for the Doctoral Program of Higher Education of China(20110022120004)the Fundamental Research Funds for the Central Universities
文摘For density inversion of gravity anomaly data, once the inversion method is determined, the main factors affecting the inversion result are the inversion parameters and subdivision scheme. A set of reasonable inversion parameters and subdivision scheme can, not only improve the inversion process efficiency, but also ensure inversion result accuracy. The gravity inversion method based on correlation searching and the golden section algorithm is an effective potential field inversion method. It can be used to invert 2D and 3D physical properties with potential data observed on flat or rough surfaces. In this paper, we introduce in detail the density inversion principles based on correlation searching and the golden section algorithm. Considering that the gold section algorithm is not globally optimized. we present a heuristic method to ensure the inversion result is globally optimized. With a series of model tests, we systematically compare and analyze the inversion result efficiency and accuracy with different parameters. Based on the model test results, we conclude the selection principles for each inversion parameter with which the inversion accuracy can be obviously improved.
