共查询到19条相似文献，搜索用时 78 毫秒
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为提高水印的不可见性,提出1种基于水印信息压缩的图像小波域水印算法.通过细化处理和骨架缩放技术将水印图像浓缩,减少要加入的水印数据量.压缩后的水印被嵌入到图像三级小波分解后的低频逼近系统上,并通过逆过程进行水印提取,嵌入的水印具有较高的不可见性和较好稳健性.仿真实验证明算法具有很好的不可见性,经滤波、噪声、图像压缩和剪切等攻击后仍然能较好的提取水印. 相似文献
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侧扫声呐回波信号是形成侧扫声呐图像的基础,是侧扫声呐系统对水下目标的最直接观测量, 将一维小波变换与非线性增强方法相结合,提出了一种基于小波变换的侧扫声呐回波信号非线性增强算法, 用以改善侧扫声呐图像对比度低、噪声强度大的问题。首先利用改进的 Bayes 阈值对侧扫声呐 ping 信号进行一维小波分解,提取信号特征信息;然后利用 2 种不同的非线性函数对高、低频小波系数进行处理;最后利用小波反变换重构信号,形成增强后的侧扫声呐图像。实测数据验证结果表明：利用该算法对侧扫声呐 ping 信号进行处理,实现了侧扫声呐图像对比度的增强和对噪声的抑制,可以获取较好的图像视觉效果。 相似文献
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利用双正交小波CDF(2,2)分解的各级小波系数能够充分体现悬沙图像中颗粒信息的特点,并结合各级小波中不同的功率分布,提出了一种对各级小波系数进行非线性调整的自适应算法。该算法能够在清除无关背景色的同时,可以最大程度地保留颗粒信息,进而获得很好的二值化图像,有利于颗粒图像的深入分析。 相似文献
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基于小波变换的声纳图像边缘特征检测研究 总被引：1，自引：0，他引：1
声纳图像的边缘特征检测是其目标识别技术的重要技术基础。声纳图像背景复杂、噪声污染严重,而传统的边缘检测方法对图像噪声非常敏感,所以针对这一特点,利用小波变换易于消除噪声、运算方便的数学特征,提出了一种基于小波变换的声纳图像边缘特征检测算法。由计算机仿真结果可以得到,与传统的边缘检测算法相比,此算法在有效地抑制噪声的同时,还可以得到较高的边缘定位精度,能够很好地检测到原始声纳图像的边缘。 相似文献
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《海洋技术学报》2018,(5)
针对水下对接过程中近距离引导和定位问题,以ARV (AutonomousRemotely Operated Vehicle)水下对接末端引导应用中对图片特征点的快速准确匹配为目标,提出结合引导灯信息的图片特征点匹配算法。在传统的ORB(Oriented FAST and Rotated BRIEF)特征点匹配算法利用关键点和描述子表征特征点的基础上,增加了相对位置信息。在特征点的提取阶段提取关键点、BRIEF描述子和相对位置信息,在匹配阶段同时考虑描述子和相对位置信息的匹配程度,提高了特征点匹配的准确率和鲁棒性。同时,在特征点提取阶段利用引导灯辅助精确计算图片的缩放尺度,使得特征点的提取更加精确、速度更快。通过实验,验证算法的有效性,证明了文中提出的算法提高了图片特征点匹配的准确率和鲁棒性,同时缩短了算法的运算时间,有利于保障定位算法的实时性,从而提升水下对接的成功率。 相似文献
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利用海岸带遥感图像提取岸线的小波变换方法 总被引：5，自引：0，他引：5
小波变换作为一门新兴的数学分支已被广泛应用于图像处理领域。本文将小波变换应用于黄河三角洲遥感图像的边缘提取 ,检测出图像的边缘 ,从而得到了三角洲岸线信息。实验结果表明基于小波变换的图像边缘提取要优于经典边缘算子的提取。此方法对于把握河口三角洲的冲淤演变规律和海岸带开发具有重要意义 相似文献
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振动信号处理技术能够提取反映结构健康的特征信息,在导管架平台健康监测中发挥了重要作用。为了给导管架平台健康监测与振动特征提取的发展提供借鉴和指导,文中对导管架平台特征提取中常用的信号处理方法:傅里叶变换、小波变换和希尔伯特-黄(Hilbert-Huang)变换进行了综述,系统总结了它们的基本理论、适用范围、优缺点以及在导管架平台健康监测中的应用情况。文中发现傅里叶变换方法简单、适用性强,对于平稳信号有很好的处理效果;小波变换和希尔伯特-黄变换能很好地提取非平稳信号的时频特性,在导管架平台健康监测与特征提取中应用广泛。同时,随着人工智能算法的快速发展,未来的发展中,可以进行信号处理方法与人工智能算法的结合使用,以期获得更好的信号处理效果。 相似文献
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Multi-beam Sonar and Side-scan Sonar compensate each other.In order to fully utilize all information,it is necessary to fuse two kinds of image and data.And the image co-registration is an important and complicated job before fusion.This paper suggests combining bathymetric data with intensity image,obtaining the characteristic points through the minimal angles of lines,and then deciding the corresponding image points by the maximal correlate coefficient in searching space.Finally,the second order polynomial is applied to the deformation model.After the images have been co-registered,Wavelet is used to fuse the images.it is shown that this algorthm can be used in the flat seafloor or the isotropic seabed.Verification is made in the paper with the observed data. 相似文献
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Precise prediction of extreme wave heights is still an evading problem whether it is done using physics based modeling or by extensively used data driven technique of Artificial Neural Network (ANN). In the present paper, Neuro Wavelet Technique (NWT) is used specifically to explore the possibility of prediction of extreme events for five major hurricanes Katrina 2005, Dean 2007, Gustav 2008, Ike 2008, Irene 2011 at four locations (NDBC wave buoys stations)1 namely; 42040, 42039, 41004, 41041 in the Gulf of Mexico. Neuro Wavelet Technique is employed by combining Discrete Wavelet Transform and Artificial Neural Networks. Discrete wavelet transform analyzes frequency of signal with respect to time at different scales. It decomposes time series into low (approximate) and high (detail) frequency components. The decomposition of approximate components (extreme events in the ocean wave series) can be carried out up to the desired multiple levels in order to provide relatively smooth varying amplitude series. This feature of wavelet transforms make it plausible for predicting extreme events with a better accuracy. In the present study third, fifth and seventh level of decompositions are used which facilitates 3 to 7 times filtering of low frequency events and seems to pay the dividend in the form of better prediction accuracy at extreme events. To develop these Neuro wavelet models to forecast the waves with lead times of 12 hr to 36 hr in advance, previously measured significant wave heights at same locations were used. The results were judged by wave plots, scatter plots and other error measures. From the results it can be concluded that the Neuro Wavelet Technique can be employed to solve the ever eluding problem of accurate forecasting of the extreme events. 相似文献
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This paper presents an investigation of the robustness of an inter-frame feature measure classifier for underwater sector scan sonar image sequences. In the initial stages the images are of either divers or remotely operated vehicles (ROV's). The inter-frame feature measures are derived from sequences of sonar scans to characterize the behavior of the objects over time. The classifier has been shown to produce error rates of 0%-2% using real nonnoisy images. The investigation looks at the robustness of the classifier with increased noise conditions and changes in the filtering of the images. It also identifies a set of features that are less susceptible to increased noise conditions and changes in the image filters. These features are the mean variance, and the variance of the rate of change in time of the intra-frame feature measures area, perimeter, compactness, maximum dimension and the first and second invariant moments of the objects. It is shown how the performance of the classifier can be improved. Success rates of up to 100% were obtained for a classifier trained under normal noise conditions, signal-to-noise ratio (SNR) around 9.5 dB, and a noisy test sequence of SNR 7.6 dB 相似文献
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The aim of this paper is to illustrate the results of a preliminary study on aerial landslide-generated waves, which has been mainly intended to establish a method for analyzing water surface records. Some simple physical experiments, reproducing the Scott Russell's wave generator, were carried out in a small two-dimensional wave flume; the Wavelet Transform (WT) is applied to analyze wave measurements and it is shown that useful information can be obtained by means of this technique. The celerity of impulsively generated waves, reflection by an overflow structure and seiching phenomena of the flume are studied. A discussion of the results along with some remarks about ongoing research is also given. 相似文献
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合成孔径雷达是海洋内波研究中最重要的工具之一。雷达图像中的斑点噪声会严重降低图像的质量,这一问题在处理和分析信号较弱的二模态内波信号和上升型内波信号时极为明显。合成孔径雷达图像中的海洋内孤立波的信号具有明显的尺度性和方向性。同时,curvelet变换作为一种同时具备尺度分辨率和方向分辨率的数学变换,能够对一幅雷达图像在不同尺度、不同方向和不同位置上进行分析。本文给出了一个基于curvelet变换的合成孔径雷达海洋内孤立波图像的斑点噪声抑制方法。该方法可简述为：(1)对一幅合成孔径雷达海洋内孤立波图像进行curvelet变换,获得curvelet系数;(2)分别仅仅保留一个尺度的系数,将其它尺度的系数置为零,利用处理之后的系数分别重建图像,得到仅仅用一个尺度的系数重建的图像;(3)分别计算上一步中得到的图像的均方差,根据波浪理论,图像的方差代表能量,方差越大则能量越大,以此可以确定内波信息集中的尺度;(4)在每个尺度下,分别计算每个方向的curvelet系数矩阵的平均值,以此确定内孤立波信号集中的方向;(5)在上两步工作的基础上,仅仅保留内波信号集中的尺度和方向的系数,而将其它尺度和方向的系数置为0,得到一幅提取主波信息的图像;(6)将上一步得到的提取主波信息的图像加回到原始图像中,从而达到增强波浪信息并抑制斑点噪声的目的。大量的实验验证表明,该方法不仅能有效地压制斑点噪声,而且能有效地增强波浪信号。 相似文献
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随着Li DAR的迅速发展,三维激光扫描技术已经被广泛运用于各个领域。点云数据配准过程中,传统的配准算法比较依赖特征点匹配精度,粗差点的存在会较大程度地影响配准精度和配准效率。通过对罗德里格矩阵、整体最小二乘原理的分析,提出了一种基于整体最小二乘的罗德里格矩阵算法。在该算法实现的过程中,能够考虑到系数矩阵误差,降低算法实现过程中特征点坐标误差对参数求解的影响。实验结果表明,本文算法比参数算法和严密的罗德里格算法精度更高、稳定性更强,在初始对应点坐标误差较大的情况下仍能获得精度较为稳定的变换参数。 相似文献