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为改进SAR图像匹配的稳健性和实时性,提出一种基于小波变换的等价图割SAR图像配准方法.该方法首先利用小波变换对图像进行分解,在低频子图像下构造等价图割,克服相干斑噪声干扰,避免NP困难,解决映射函数选取问题,从图像中分割出精确目标.其次利用尺度不变特征变换(SIFT)方法实现目标的特征匹配,降低搜索空间特征点描述,提高实时性.最后通过匹配关系找到变换参数,实现图像精确配准.实验结果表明,该方法能快速而精确地实现SAR图像配准. 相似文献
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基于虚拟点的可见光和SAR图像配准研究 总被引:1,自引:1,他引:0
本文以机场场景下的可见光和SAR图像为研究对象,提出了一种基于虚拟点特征的可见光和SAR图像配准方法.该方法以虚拟点特征和控制点匹配技术为基础,处理具有全局仿射几何失真的异源图像配准问题.首先根据两类图像的特点,使用Canny算子和一种兴趣算子提取两幅图像中的共有特征一直线特征,然后在直线特征的基础上拟合虚拟点特征,采用基于特征一致的粗配准和基于虚拟点特征的精确配准相结合的方法,对两幅图像实现由粗到精的自动配准,实验结果表明,本文方法可行且能取得较高的配准精度. 相似文献
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基于仿射不变SIFT特征的SAR图像配准 总被引:2,自引:2,他引:0
针对SAR(Synthetic Aperture Radar)图像全自动配准问题,本文提出一种基于仿射不变SIFT(Scale Invariant Feature Transform)特征的精确配准方法.该方法首先对传统SIFT方法改进构建具有仿射不变性的SIFT描述子,并利用该描述子对提取的控制点进行粗匹配,然后由粗匹配点对的尺度比和方位差及其邻域的灰度相似性构建新的相似矩阵,最后利用SVD(Singular Value Decomposition)方法确定精确匹配点对,求出变换参数从而实现图像的精确配准.实验结果表明该方法优于传统的SIFT方法和SIFT+SVD方法并且可以达到亚像素的配准精度. 相似文献
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多源图像配准技术分析与展望 总被引:48,自引:2,他引:48
在给出多源图像配准的定义后,将常见的图像配准方法分为基于图像灰度的方法和基于图像特征的方法两大类。以此为分类基础,对国内外现有的图像配准技术和方法进行了分析和评述,并重点介绍了基于图像特征的配准方法。随后对图像配准技术所面临的主要难题作了分析。最后介绍了图像配准在军事、遥感、医学等领域的应用,并展望了其未来的发展。 相似文献
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合成孔径雷达(SAR)的成像过程使其高分辨图像的几何形变呈现局部性.针对高分辨SAR图像精确配准问题,本文提出一种基于邻域重构模型的局部转换函数.邻域重构模型首先采用重构系数刻画参考图像中每个像素点的几何位置;接着给出了一种重构控制点的选择标准使每个像素的配准误差达到最小;最后根据重构系数及控制点坐标对输入图像进行再抽样以实现配准.与经典分片线性映射相比,该模型从理论上给出了一种区域剖分准则:对于每个像素选取能使配准误差能达到最小的控制点作为重构控制点.对模拟数据和真实SAR图像进行了试验,结果表明,该模型能有效地提高配准精度. 相似文献
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针对发生较大角度旋转及平移时图像配准精度不高,图像配准对局部形变和光照较为敏感的问题,本文提出了基于直线和SURF特征的图像分区域配准算法。首先利用Hough变换实现图像的粗配准;然后对图像进行分区,在子区域内利用SURF算子求取变换模型参数,完成图像的配准。实验表明该方法可用于红外与可见光图像的配准,与传统方法相比,本方法能够在图像存在大角度旋转和平移时实现高精度配准,且在图像存在局部形变及光照不均时精度较好。 相似文献
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海底地貌测绘的最新技术是利用干涉合成孔径声纳(InSAS)进行三维立体成像,其信号处理的关键在于成像之后的图像配准、干涉成像等步骤。图像配准的精度直接影响相位图的质量,从而影响所求目标区域高度的准确性。在分析InSAS复图像数据特点的基础上给出一种实用的基于最大干涉频谱法的复图像配准方法,缩减了配准搜索范围,在一定程度上提高了配准速度和精度。利用该方法对湖试测得的湖底地貌数据进行配准,用复图像相干系数和干涉相位图的残余点数对配准效果进行评估,通过配准前后的参数比较,证实了最大干涉频谱法用于InSAS复图像配准能够取得较好的配准效果。 相似文献
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A novel approach to obtain precise segmentation of synthetic aperture radar (SAR) images using Markov random field model on region adjacency graph (MRF-RAG) is presented. First, to form a RAG, the watershed algorithm is employed to obtain an initially over-segmented image. Then, a novel MRF is defined over the RAG instead of pixels so that the erroneous segmentation caused by speckle in SAR images can be avoided and the number of configurations for the combinatorial optimisation can be reduced. Finally, a modification method based on Gibbs sampler is proposed to correct edge errors, brought by the over-segmented algorithm, in the segmentations obtained by MRF-RAG. The experimental results both on simulated and real SAR images show that the proposed method can reduce the computational complexity greatly as well as increase the segmentation precision. 相似文献
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基于 NSCT 域特征和 PCNN 的SAR 图像目标分割 总被引:1,自引:0,他引:1
针对 SAR 图像的目标自动分割问题,在分析非下采样轮廓波变换和脉冲耦合神经网络的基础上,提出了一种基于非下采样轮廓波域特征图和 PCNN 的 SAR 图像目标分割算法.对 SAR 图像经过 NSCT 分解后的高、低频图像分别运用不同方式进行处理.对低频图用 PCNN 进行分割以获取目标所在的区域,对高频子带构造了特征图,对特征图利用 PCNN 进行分割以获取目标的精细结构.利用 MSTAR 数据进行了仿真实验,并与基于模糊 C 均值的分割算法、基于马尔可夫随机场的分割算法进行了对比.实验结果表明,所提出算法对 SAR 图像目标的分割结果更为准确,同时较其它算法具有更强的抗噪性能,是一种有效可行的 SAR 目标分割算法. 相似文献
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Li-Jen Du Jong-Sen Lee Karl Hoppel Stephen A. Mango 《International journal of imaging systems and technology》1992,4(4):319-326
Multiresolution representation of images using the wavelet transform is a new approach for the analysis of image information content. The transform can be computed efficiently by a pyramidal algorithm based on convolution with quadrature mirror filters. The result is a set of subband images which consists of a lower resolution version of the original image and a sequence of detail images containing higher frequency spectral information. We used this representation for the supervised segmentation of polarimetric SAR images of the San Francisco Bay area acquired by the airborne JPL system for identifying various terrain covers. Since the wavelet transform generates the localized spatial and spectral information simultaneously, detailed knowledge of the texture variations within an image can be extracted from the data in the spectral subbands. The segmentation algorithm developed in this paper is formulated by taking into consideration both the intensity and the texture information. For polarimetric SAR images, the classification accuracy can be enhanced, if the combined data from copolarized and cross-polarized images are used in the discrimination process. In contrast to other texture segmentation approaches, this algorithm does not require extensive calculations.©1993 John Wiley & Sons Inc 相似文献
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《Advanced Powder Technology》2021,32(10):3885-3903
Mineral image segmentation plays a vital role in the realization of machine vision based intelligent ore sorting equipment. However, the existing image segmentation methods still cannot effectively solve the problem of adhesion and overlap between mineral particles, and the segmentation performance of small and irregular particles still needs to be improved. To overcome these bottlenecks, we propose a deep learning based image segmentation method to segment the key areas in mineral images using morphological transformation to process mineral image masks. This investigation explores four aspects of the deep learning-based mineral image segmentation model, including backbone selection, module configuration, loss function construction, and its application in mineral image classification. Specifically, referring to the designs of U-Net, FCN, Seg Net, PSP Net, and DeepLab Net, this experiment uses different backbones as Encoder to building ten mineral image segmentation models with different layers, structures, and sampling methods. Simultaneously, we propose a new loss function suitable for mineral image segmentation and compare CNNs-based segmentation models' training performance under different loss functions. The experiment results show that the proposed mineral image segmentation has excellent segmentation performance, effectively solves adhesion and overlap between adjacent particles without affecting the classification accuracy. By using the Mobile Net as backbone, the PSP Net and DeepLab can achieve a high segmentation performance in mineral image segmentation tasks, and the 15 × 15 is the most suitable size for erosion element structure to process the mask images of the segmentation models. 相似文献
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Li H Bochko V Jaaskelainen T Parkkinen J Shen IF 《Journal of the Optical Society of America. A, Optics, image science, and vision》2008,25(11):2805-2816
In this work, we propose a new algorithm for spectral color image segmentation based on the use of a kernel matrix. A cost function for spectral kernel clustering is introduced to measure the correlation between clusters. An efficient multiscale method is presented for accelerating spectral color image segmentation. The multiscale strategy uses the lattice geometry of images to construct an image pyramid whose hierarchy provides a framework for rapidly estimating eigenvectors of normalized kernel matrices. To prevent the boundaries from deteriorating, the image size on the top level of the pyramid is generally required to be around 75 x 75, where the eigenvectors of normalized kernel matrices would be approximately solved by the Nystr?m method. Within this hierarchical structure, the coarse solution is increasingly propagated to finer levels and is refined using subspace iteration. In addition, to make full use of the abundant color information contained in spectral color images, we propose using spectrum extension to incorporate the geometric features of spectra into similarity measures. Experimental results have shown that the proposed method can perform significantly well in spectral color image segmentation as well as speed up the approximation of the eigenvectors of normalized kernel matrices. 相似文献
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《成像科学杂志》2013,61(7):579-591
AbstractLow brightness contrast and grey level discontinuities of the ultrasonic liver image make it difficult to segment the object and the background and to extract the edges of the object using the global optimal threshold method. In this paper, we investigate a local optimal threshold method for the segmentation of ultrasound liver image. First of all, the distributed energy of the ultrasound liver image is estimated in the proposed liver segmentation. Then, the polynomials are fitted from the distributed energy data and a peak zone is defined from the minimum of the fitted polynomials. Finally, a few blocked images are divided from the number of the peak zones. Furthermore, multiple local optimal thresholds are obtained from the blocked images using Otsu’s method, and the ultrasonic liver image is segmented according to all local optimal thresholds. Experimental results validate the segmentation and edge detection of liver in the ultrasound images. 相似文献
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The synthetic aperture radar (SAR) images are mainly affected by speckle noise. Speckle degrades the features in the image and reduces the ability of a human observer to resolve fine detail, hence despeckling is very much required for SAR images. This paper presents speckle noise reduction in SAR images using a combination of curvelet and fuzzy logic technique to restore speckle-affected images. This method overcomes the limitation of discontinuity in hard threshold and permanent deviation in soft threshold. First, it decomposes noise image into different frequency scales using curvelet transform, and then applies the fuzzy shrinking technique to high-frequency coefficients to restore noise-contaminated coefficients. The proposed method does not use threshold approach only by proper selection of shrinking parameter the speckle in SAR image is suppressed. The experiment is carried out on different resolutions of RISAT-1 SAR images, and results are compared with the existing filtering algorithms in terms of noise mean variance (NMV), mean square difference (MSD), equal number of looks (ENL), noise standard deviation (NSD) and speckle suppression index (SSI). A comparison of the results shows that the proposed technique suppresses noise significantly, preserves the details of the image and improves the visual quality of the image. 相似文献
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FPGA implementation of particle swarm optimization based on new fitness function for MRI images segmentation 下载免费PDF全文
Fayçal Hamdaoui Anis Sakly Abdellatif Mtibaa 《International journal of imaging systems and technology》2015,25(2):139-147
Magnetic resonance imaging (MRI) is considered as a key part in therapeutic procedures because it clearly defines the aim. It also avoids sensitive organs and it determines the desired paths. This phenomenon requires image processing operations such as segmentation to locate the tumor. Medical image segmentation is still an important topic in the field of brain tumor. In the present article, we propose a Hardware Architecture of segmentation based on a Modified Particle Swarm Optimization (HAMPSO) algorithm for MRI images segmentation. To achieve this, we use the Xilinx System Generator (XSG) to be implemented on a Field Programmable Gate Array (FPGA). This architecture is based on a new variant of objective function. These performances of the proposed method are proved using a set of MRI images and were compared to the Hardware Architecture of segmentation based on Particle Swarm Optimization (HAPSO) in terms of either device utilization, execution time, qualitatively or quantitatively results. 相似文献
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Tharcis Paulraj Kezi Selva Vijila Chelliah Sundar Chinnasamy 《International journal of imaging systems and technology》2019,29(3):374-381
Soft computing is an associate rising field that plays a crucial half in the area of engineering and science. One of the most significant applications of soft computing is image segmentation. It focuses on an exploiting tolerance of imprecision and uncertainty. Segmentation supported soft computing remains a difficult task within the medical field. Medical images are habitually used in the segmentation process to extract the meaningful portions and to know and clarify the condition of the particular patient. In this article, we implement an efficient possibilistic fuzzy C-means (PFCM) approach to segment the lung portion in the computed tomography (CT) image and the result shows that it improves the segmentation accuracy upto 98.5012% and results are compared with existing segmenting approaches like fuzzy possibilistic C-means method, fuzzy bitplane method and so forth. Also, the PFCM approach increases the diagnostic accuracy of the computer aided diagnosis system using CT images. The radiologist may utilize this computer aided diagnosis system results as a second opinion of their diagnosed results. 相似文献