In this study, we extend our previous adaptive steganographic algorithm to support point geometry. For the purpose of the vertex decimation process presented in the previous work, the neighboring information between p...In this study, we extend our previous adaptive steganographic algorithm to support point geometry. For the purpose of the vertex decimation process presented in the previous work, the neighboring information between points is necessary. Therefore, a nearest neighbors search scheme, considering the local complexity of the processing point, is used to determinate the neighbors for each point in a point geometry. With the constructed virtual connectivity, the secret message can be embedded successfully after the vertex decimation and data embedding processes. The experimental results show that the proposed algorithm can preserve the advantages of previous work, including higher estimation accuracy, high embedding capacity, acceptable model distortion, and robustness against similarity transformation attacks. Most importantly, this work is the first 3D steganographic algorithm for point geometry with adaptation.展开更多
Image matching refers to the process of matching two or more images obtained at different time,different sensors or different conditions through a large number of feature points in the image.At present,image matching ...Image matching refers to the process of matching two or more images obtained at different time,different sensors or different conditions through a large number of feature points in the image.At present,image matching is widely used in target recognition and tracking,indoor positioning and navigation.Local features missing,however,often occurs in color images taken in dark light,making the extracted feature points greatly reduced in number,so as to affect image matching and even fail the target recognition.An unsharp masking(USM)based denoising model is established and a local adaptive enhancement algorithm is proposed to achieve feature point compensation by strengthening local features of the dark image in order to increase amount of image information effectively.Fast library for approximate nearest neighbors(FLANN)and random sample consensus(RANSAC)are image matching algorithms.Experimental results show that the number of effective feature points obtained by the proposed algorithm from images in dark light environment is increased,and the accuracy of image matching can be improved obviously.展开更多
密度峰值聚类(density peaks clustering,DPC)是一种基于密度的聚类算法,该算法可以直观地确定类簇数量,识别任意形状的类簇,并且自动检测、排除异常点.然而,DPC仍存在些许不足:一方面,DPC算法仅考虑全局分布,在类簇密度差距较大的数据...密度峰值聚类(density peaks clustering,DPC)是一种基于密度的聚类算法,该算法可以直观地确定类簇数量,识别任意形状的类簇,并且自动检测、排除异常点.然而,DPC仍存在些许不足:一方面,DPC算法仅考虑全局分布,在类簇密度差距较大的数据集聚类效果较差;另一方面,DPC中点的分配策略容易导致“多米诺效应”.为此,基于代表点(representative points)与K近邻(K-nearest neighbors,KNN)提出了RKNN-DPC算法.首先,构造了K近邻密度,再引入代表点刻画样本的全局分布,提出了新的局部密度;然后,利用样本的K近邻信息,提出一种加权的K近邻分配策略以缓解“多米诺效应”;最后,在人工数据集和真实数据集上与5种聚类算法进行了对比实验,实验结果表明,所提出的RKNN-DPC可以更准确地识别类簇中心并且获得更好的聚类结果.展开更多
针对三维激光点云线性K最近邻(K-nearest neighbor, KNN)搜索耗时长的问题,提出了一种利用多处理器片上系统(multi-processor system on chip, MPSoC)现场可编程门阵列(field-programmable gate array,FPGA)实现三维激光点云KNN快速搜...针对三维激光点云线性K最近邻(K-nearest neighbor, KNN)搜索耗时长的问题,提出了一种利用多处理器片上系统(multi-processor system on chip, MPSoC)现场可编程门阵列(field-programmable gate array,FPGA)实现三维激光点云KNN快速搜索的方法。首先给出了三维激光点云KNN算法的MPSoC FPGA实现框架;然后详细阐述了每个模块的设计思路及实现过程;最后利用MZU15A开发板和天眸16线旋转机械激光雷达搭建了测试平台,完成了三维激光点云KNN算法MPSoC FPGA加速的测试验证。实验结果表明:基于MPSoC FPGA实现的三维激光点云KNN算法能在保证邻近点搜索精度的情况下,减少邻近点搜索耗时。展开更多
针对目前隧道无序点云法线全局定向方法存在的问题,提出一种基于优先级队列的快速法线全局定向方法。首先,针对传统方法采用近邻搜索方法算法复杂度为O(lgn)的问题,提出一种新的Search Data Struct(SDS)空间搜索数据结构用于近邻搜索,...针对目前隧道无序点云法线全局定向方法存在的问题,提出一种基于优先级队列的快速法线全局定向方法。首先,针对传统方法采用近邻搜索方法算法复杂度为O(lgn)的问题,提出一种新的Search Data Struct(SDS)空间搜索数据结构用于近邻搜索,将算法复杂度降低到O(n),提升了海量点云的搜索效率;其次,针对传统方法计算复杂且不鲁棒等问题,提出一种新的优先级队列结构,优先级队列容纳多级类别,克服了传统方法的缺点;最后,针对传统方法需要对全局点云进行多次判断和效率低的问题,采用优先级队列策略和区域增长方法,引导点云沿着最平坦的方向进行法线定向,保证点云在奇异情况下定向正常,确保点云整体法线方向的一致性,同时每个点仅需进行一次判断即可完成定向,将算法复杂度降低到O(n),提高了法线全局定向的效率。试验结果表明,本文提出的算法效果与商业软件GeoMagic的效果相当,能够处理隧道无序点云的各种奇异情况,算法在平缓区域、尖锐特征区域和高曲率区域能得到正确的法线方向,且效率相对GeoMagic提高了14倍,大幅提升了无序点云处理的工程化水平。展开更多
基金supported by the National Science Council under Grant No. NSC98-2221-E-468-017 and NSC 100-2221-E-468-023the Research Project of Asia University under Grant No. 100-A-04
文摘In this study, we extend our previous adaptive steganographic algorithm to support point geometry. For the purpose of the vertex decimation process presented in the previous work, the neighboring information between points is necessary. Therefore, a nearest neighbors search scheme, considering the local complexity of the processing point, is used to determinate the neighbors for each point in a point geometry. With the constructed virtual connectivity, the secret message can be embedded successfully after the vertex decimation and data embedding processes. The experimental results show that the proposed algorithm can preserve the advantages of previous work, including higher estimation accuracy, high embedding capacity, acceptable model distortion, and robustness against similarity transformation attacks. Most importantly, this work is the first 3D steganographic algorithm for point geometry with adaptation.
基金Supported by the National Natural Science Foundation of China(No.61771186)the Heilongjiang Provincial Natural Science Foundation of China(No.YQ2020F012)the University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province(No.UNPYSCT-2017125).
文摘Image matching refers to the process of matching two or more images obtained at different time,different sensors or different conditions through a large number of feature points in the image.At present,image matching is widely used in target recognition and tracking,indoor positioning and navigation.Local features missing,however,often occurs in color images taken in dark light,making the extracted feature points greatly reduced in number,so as to affect image matching and even fail the target recognition.An unsharp masking(USM)based denoising model is established and a local adaptive enhancement algorithm is proposed to achieve feature point compensation by strengthening local features of the dark image in order to increase amount of image information effectively.Fast library for approximate nearest neighbors(FLANN)and random sample consensus(RANSAC)are image matching algorithms.Experimental results show that the number of effective feature points obtained by the proposed algorithm from images in dark light environment is increased,and the accuracy of image matching can be improved obviously.
文摘密度峰值聚类(density peaks clustering,DPC)是一种基于密度的聚类算法,该算法可以直观地确定类簇数量,识别任意形状的类簇,并且自动检测、排除异常点.然而,DPC仍存在些许不足:一方面,DPC算法仅考虑全局分布,在类簇密度差距较大的数据集聚类效果较差;另一方面,DPC中点的分配策略容易导致“多米诺效应”.为此,基于代表点(representative points)与K近邻(K-nearest neighbors,KNN)提出了RKNN-DPC算法.首先,构造了K近邻密度,再引入代表点刻画样本的全局分布,提出了新的局部密度;然后,利用样本的K近邻信息,提出一种加权的K近邻分配策略以缓解“多米诺效应”;最后,在人工数据集和真实数据集上与5种聚类算法进行了对比实验,实验结果表明,所提出的RKNN-DPC可以更准确地识别类簇中心并且获得更好的聚类结果.
文摘针对目前隧道无序点云法线全局定向方法存在的问题,提出一种基于优先级队列的快速法线全局定向方法。首先,针对传统方法采用近邻搜索方法算法复杂度为O(lgn)的问题,提出一种新的Search Data Struct(SDS)空间搜索数据结构用于近邻搜索,将算法复杂度降低到O(n),提升了海量点云的搜索效率;其次,针对传统方法计算复杂且不鲁棒等问题,提出一种新的优先级队列结构,优先级队列容纳多级类别,克服了传统方法的缺点;最后,针对传统方法需要对全局点云进行多次判断和效率低的问题,采用优先级队列策略和区域增长方法,引导点云沿着最平坦的方向进行法线定向,保证点云在奇异情况下定向正常,确保点云整体法线方向的一致性,同时每个点仅需进行一次判断即可完成定向,将算法复杂度降低到O(n),提高了法线全局定向的效率。试验结果表明,本文提出的算法效果与商业软件GeoMagic的效果相当,能够处理隧道无序点云的各种奇异情况,算法在平缓区域、尖锐特征区域和高曲率区域能得到正确的法线方向,且效率相对GeoMagic提高了14倍,大幅提升了无序点云处理的工程化水平。
