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基于二维灰度直方图的蚁群图像分割 总被引:6,自引:0,他引:6
提出了一种基于二维灰度直方图的蚁群图像分割方法。该方法基于二维灰度直方图
的灰度、邻域平均灰度及灰度频数进行蚁群模糊聚类,通过二维灰度直方图的一维最佳投影,设置精确的初始聚类中心来解决蚁群算法循环次数多、计算量大的问题;并针对具体应用,对聚类半径、信息激素和启发式引导函数进行了相应的修正。实验表明该算法速度快、划分特性好,可以准确地分割出目标。 相似文献
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现有的图像分割算法存在着耗时量大,分割效果不佳等问题,不适用与红外系统领域的应用。针对上述问题,根据灰度级-梯度二维直方图的目标分割优势,通过与蚁群算法相结合,提出了一种结合蚁群算法与二维直方图的红外图像分割算法。通过在传统的灰度-梯度二维直方图进行引入边缘与噪声区域的相关量;通过将图像窗口化,并根据最佳分割阈值对蚁群的启发函数以及信息素更新进行重新定义,来实现红外目标的快速提取。实验结果表明,该算法分割后的红外目标边缘清晰,抗干扰能力较强,且运算速度也得到了有效提高。 相似文献
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针对高清电视数字图像边缘对比度低、清晰显示难度大的问题,提出考虑视觉传达效果的二维灰度电视图像直方图边缘显示方法.首先对包含大量扰动信息和各类噪声的电视图像进行预处理,识别信号模式并进行特征提取;根据电视图像特征及灰度梯度在图像边缘的响应,获取直方图的统计特征并进行分块处理,形成二维灰度图像的直方图;搜寻出二维灰度图像直方图与电视数字图像边缘的内在关联性,实现对电视图像边缘的精确显示.实验结果证明,提出的电视图像边缘显示方法能够获得更好的视觉传达效果. 相似文献
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分析了雷达目标散射信号特征,在此基础上,根据雷达图像的特点,采用适当的图像处理方式形成雷达二维灰度图,最后通过图像矩阵描述方法进行特征提取。 相似文献
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阈值法是图像分割中的重要方法,并在图像处理中得到了广泛的应用。针对电子扫描显微镜(SEM)摄取的纤维材料图像的自身特性,在预处理的基础上,提出了一种基于二维灰度直方图的人工鱼群图像分割方法。二维直方图的阈值的选取,是一个求全局最优的优化问题,本文将人工鱼群的算法应用于图像分割中,利用人工鱼群算法寻求二维熵的最优值,在实验中,人工鱼群算法收敛速度快,结果稳定,取得了理想的效果。 相似文献
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传统的二维Otsu法采用灰度级-平均灰度级二维直方图,针对传统二维直方图区域划分存在的不足,基于灰度级-梯度二维直方图,提出了一种引入积分图像的快速二维Otsu法,利用积分图像降低搜索二维直方图最佳阈值的计算复杂度,从而减少了计算量。实验结果表明,该方法具有良好的分割效果,大大地提高了计算速度,是一种快速有效且实时性好的阈值分割算法。 相似文献
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基于OTSU的近红外图像分割的应用研究 总被引:1,自引:1,他引:1
OTSU法是最常用的利用图像的一维灰度直方图的阈值化方法之一,二维OTSU除了考虑像素点的灰度信息外还考虑了像素点与其邻域的空间信息.由于红外图像不同于一般灰度图像的一些特征,有针对性的采用了二维OTSU对其进行分割.通过与一维OTSU的相互比较可以看出,二维OTSU对近红外图像拥有更好的分割效果,能有效的分割出炉管图... 相似文献
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针对目标红外图像与可见光图像信息优势互补的需 求,引入改进的脉冲耦合神经网络,提出一种新颖的基于非下采样 剪切波变换的红外与可见光图像融合算法。首先选取非下采样剪切波变换将图像进行分解, 获得高低频分量;其次低频分量的 融合是利用改进空间频率作用脉冲耦合神经网络输入激励,且其链接强度由表征图像信息的 平均梯度自适应调整;而高频分量 处理方法是利用局部平均梯度与区域方差自适应加权融合;最后,对分别处理后的低高频分 量经过非下采样剪切波变换可逆变 换获取融合图像。实验结果表明,该算法可以有效综合图像的优势信息,融合结果在主观与 客观评价上比经典算法更好。 相似文献
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为了研究反向传播人工神经网络(BP-ANN,back-propagation artificial neural network)对光学相干层析(OCT)图像的分类能力以及用不同算法训练的网络之间的性能差异,设计了基于纹理特征分析的BP-ANN图像分类实验系统。针对不同图像集,系统可根据类内和类间分散度的比值自适应地筛选最具区分性的纹理特征组成特征向量,再利用以不同算法训练的BP-ANN进行分类。实验表明,BP-ANN在经过快速训练后可以有效分辨不同组织图像,而Levenberg-Mar-quardt(LM)算法则被认为是最为有效的训练算法。以LM算法训练的BP-ANN可以在1 s内以平均8次的迭代计算完成训练,对测试集的分类准确率可以达到93.0%。 相似文献
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We propose a pattern classification based approach for simultaneous three-dimensional (3-D) object modeling and segmentation in image volumes. The 3-D objects are described as a set of overlapping ellipsoids. The segmentation relies on the geometrical model and graylevel statistics. The characteristic parameters of the ellipsoids and of the graylevel statistics are embedded in a radial basis function (RBF) network and they are found by means of unsupervised training. A new robust training algorithm for RBF networks based on alpha-trimmed mean statistics is employed in this study. The extension of the Hough transform algorithm in the 3-D space by employing a spherical coordinate system is used for ellipsoidal center estimation. We study the performance of the proposed algorithm and we present results when segmenting a stack of microscopy images. 相似文献
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《IEEE transactions on medical imaging》2008,27(12):1791-1810
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In this paper, we propose a novel unsupervised video object extraction algorithm for individual images or image sequences with low depth of field (DOF). Low DOF is a popular photographic technique which enables the representation of the photographer's intention by giving a clear focus only on an object of interest (OOI). We first describe a fast and efficient scheme for extracting OOIs from individual low‐DOF images and then extend it to deal with image sequences with low DOF in the next part. The basic algorithm unfolds into three modules. In the first module, a higher‐order statistics map, which represents the spatial distribution of the high‐frequency components, is obtained from an input low‐DOF image. The second module locates the block‐based OOI for further processing. Using the block‐based OOI, the final OOI is obtained with pixel‐level accuracy. We also present an algorithm to extend the extraction scheme to image sequences with low DOF. The proposed system does not require any user assistance to determine the initial OOI. This is possible due to the use of low‐DOF images. The experimental results indicate that the proposed algorithm can serve as an effective tool for applications, such as 2D to 3D and photorealistic video scene generation. 相似文献
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Chengpu Yu Cishen Zhang Lihua Xie 《Multidimensional Systems and Signal Processing》2012,23(4):499-513
Speckle noise of ultrasound images is of multiplicative nature which degrades the image quality in terms of resolution and contrast. While there exist a number of algorithms for reduction of multiplicative Rayleigh distributed random speckle noise, the low signal-to-noise ratio (SNR) issue of the multiplicative Rayleigh noise is still not adequately resolved. In this paper, a simple 2-dimensional (2D) local intensity smoothing method is presented which transforms the Rayleigh noise contaminated in ultrasound images to Nakagami distributed noise so as to improve the SNR of processed images. A 2D total variation regularized Nakagami speckle reduction algorithm is derived based on the maximum a posteriori estimation framework, which performs well in restoring piecewise-smooth reflectivity and preserving fine details of the image. The proposed algorithm is verified by a series of computer-simulated and real ultrasound image data. It is shown that the algorithm considerably improves the quality of ultrasound images and outperforms the Rayleigh noise based speckle reduction methods in terms of speckle SNR and contrast-to-noise ratio. 相似文献
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提出了一种新的自适应图像增强融合算法,算法将输入图像分解为反映图像平均照度特征的低频分量和反映图像对比度特征的高频分量图像,基于局部灰度方差增强因子和非线性投影变换因子进行自适应图像增强处理,基于局部能量、局部归一化互相关融合测度和加权融合算子对增强后的图像进行特征提取和重构,得到融合图像.有效地将输入图像细节特征传递组合到融合图像中. 相似文献
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Youssef Douini Jamal Riffi Adnane Mohamed Mahraz Hamid Tairi 《Signal, Image and Video Processing》2017,11(7):1321-1328
Image registration is defined as an important process in image processing in order to align two or more images. A new image registration algorithm for translated and rotated pairs of 2D images is presented in order to achieve subpixel accuracy and spend a small fraction of computation time. To achieve the accurate rotation estimation, we propose a two-step method. The first step uses the Fourier Mellin Transform and phase correlation technique to get the large rotation, then the second one uses the Fourier Mellin Transform combined with an enhance Lucas–Kanade technique to estimate the accurate rotation. For the subpixel translation estimation, the proposed algorithm suggests an improved Hanning window as a preprocessing task to reduce the noise in images then achieves a subpixel registration in two steps. The first step uses the spatial domain approach which consists of locating the peak of the cross-correlation surface, while the second uses the frequency domain approach, based on low-frequency (aliasing-free part) of aliased images. Experimental results presented in this work show that the proposed algorithm reduces the computational complexities with a better accuracy compared to other subpixel registration algorithms. 相似文献