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生物芯片图像样点的自动识别 总被引:6,自引:1,他引:6
样点自动识别是生物芯片信息自动提取的关键。根据样点、噪声和背景特征的关系提出一种新的自动识别方法。使用数学形态学和均值算子相结合的方法实现图像的滤波增强和背景亮度的估计;通过对功率谱的分析实现图像的倾斜校正和样点中心的网格定位;利用样点边缘亮度与均方差的特征实现样点中心和半径的校正。多幅生物芯片处理的实验证明该方法具有良好的抗噪声能力和弱信号辨识能力,能快速、准确地实现样点自动识别. 相似文献
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本文提出了一种基于形态学和小波域杂波抑制的微弱目标检测方法,该方法将图像序列进行形态擘tophat滤波,然后小波变换,再分别对各小波子带作平滑滤波,按各子带对滤波前后小波系数作差分运算,最后经过小波逆变换得到具有微弱目标的残差图像序列.用残差图像tophat结果估计目标潜在区域,在目标潜在域的约束下,对残差图像序列进行时空域数据融合,实现微弱运动目标的检测.仿真实验表明,该方法杂波抑制后残差图像具有很好的白高斯特性,且目标邻域信杂比(scNR)的平均增益比图像空域平滑滤波和图像频域低通滤波等典型运算的SCNR平均增益有明显改善,目标检测算法在5帧图像集成时能稳定检测出微弱运动目标轨迹. 相似文献
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针对存在背景干扰和噪声情况下的红外图像弱小目标检测问题,提出了基于双树复小波变换和混沌粒子群优化的检测方法。该方法一方面先基于双树复小波变换对原始图像进行去噪,再利用Top-hat算子抑制背景;另一方面先利用Top-hat算子抑制原始图像的背景,经双树复小波去噪后,再进一步使用Top-hat算子。将上述两方面得到的图像求和即为预处理图像。然后基于混沌粒子群优化的类内绝对差及背景与目标面积差的阈值选取方法分割预处理图像。大量实验结果表明,与基于小波和形态学的红外目标检测方法相比,该方法抗噪性强,具有更为优越的检测性能。 相似文献
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为准确提取水下爆炸气泡图像边界,尝试采用在边界识别中常用的canny算子边缘检测方法,但结果并不理想。在此基础上提出了先将灰度图像转换成索引图像,采用二维离散小波分解方法小波变换方法对索引图像进行分解,针对分解的图像轮廓部分主要体现在低频部分的特点对低频部分进行增强处理,对高频部分进行衰减处理,然后对处理后的图像进行canny算子边缘检测。结果表明:小波增强后的canny算子边缘检测方法处理出来的气泡图像边界清晰,可作为进一步分析研究的依据。 相似文献
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给出了Daubechies小波经Lazy小波七步提升的具体表示,将双正交滤波的提升格式用算子形式表出后,给出了多步提升逆的一种快速作法。 相似文献
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改进提升小波变换的空间频率比图像融合 总被引:4,自引:1,他引:3
提出了一种新型图像融合算法.该算法在提升小波变换的基础上,通过取消其奇偶分裂环节,得到具有平移不变性的非采样提升小波变换.对图像经非采样提升小波变换后的低频分量首先定义一种空间频率比,再通过空间频率比来计算融合因子,然后采用加权与选择相结合的方法对低频分量进行融合.高频分量直接选择一种基于边缘信息的加权融合方法.最后通过非采样提升小波逆变换重构得到融合图像.实验结果显示,该算法相对传统的图像融合算法能更好地描述灰度的突变信息,获得含有丰富细节特征的融合图像. 相似文献
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基于提升模式的特征小波构造及其应用 总被引:3,自引:0,他引:3
为了获得期望特性的特征小波,采用提升模式构造了一种新小波。在提升模式的框架下,以3次B样条小波变换的低通滤波器作为初始滤波器,采用插值细分原理设计提升算子,一次提升之后获得了新的小波。这种小波继承了初始滤波器的低通滤波的特性,又具有提取瞬态冲击特征的能力。对提升模式框架进行等效易位变换,再去除抽样算子,提出了一种基于提升模式的非抽样小波变换算法。采用新小波的非抽样小波变换较好地提取了压缩机齿轮箱摩擦和高压缸碰摩的故障特征。工程实践证明,与传统离散小波变换相比,非抽样小波变换分解结果能够提供更加丰富的诊断信息。 相似文献
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分形理论在图像的纹理识别中得到了广泛应用,由于分形维数不能反映图像的空间信息,容易造成误识别。针对该问题并结合声纳图像的特点,通过提升结构构造了Haar小波,并将提升小波变换同分形理论相结合,利用小波分解的多分辨率特点和分形维数的多尺度特性,提高图像的识别率。采用Levenberg-Marquardt(L-M)算法优化的BP神经网络对不同信噪比的声纳图像进行分类识别。实验结果表明,文中方法不论在识别率还是识别时间上均优于传统纹理识别方法。 相似文献
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In JPEG2000, the Cohen–Daubechies–Feauveau (CDF) 9/7‐tap wavelet filter implemented by using the conventional lifting scheme has two problems. The first problem is that the filter coefficients are remaining complex; second, the conventional lifting scheme ignores image edges in the coding process. In this article, we propose an effective wavelet lifting scheme to solve these problems. For this purpose, we design the optimal 9/7‐tap wavelet filters in two steps. In the first step, we select the appropriate filter coefficients; in the second step, we employ a median operator to consider image edges. Experimental results from using the median lifting scheme and combining filter optimization and median lifting show that our proposed methods outperform the well‐known CDF 9/7‐tap wavelet filter of JPEG2000 on edge‐dominant images. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 359–366, 2010 相似文献
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Automated segmentation of retinal blood vessels and identification of proliferative diabetic retinopathy 总被引:1,自引:0,他引:1
Jelinek HF Cree MJ Leandro JJ Soares JV Cesar RM Luckie A 《Journal of the Optical Society of America. A, Optics, image science, and vision》2007,24(5):1448-1456
Proliferative diabetic retinopathy can lead to blindness. However, early recognition allows appropriate, timely intervention. Fluorescein-labeled retinal blood vessels of 27 digital images were automatically segmented using the Gabor wavelet transform and classified using traditional features such as area, perimeter, and an additional five morphological features based on the derivatives-of-Gaussian wavelet-derived data. Discriminant analysis indicated that traditional features do not detect early proliferative retinopathy. The best single feature for discrimination was the wavelet curvature with an area under the curve (AUC) of 0.76. Linear discriminant analysis with a selection of six features achieved an AUC of 0.90 (0.73-0.97, 95% confidence interval). The wavelet method was able to segment retinal blood vessels and classify the images according to the presence or absence of proliferative retinopathy. 相似文献
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《成像科学杂志》2013,61(7):408-422
AbstractImage fusion is a challenging area of research with a variety of applications. The process of image fusion collects information from different sources and combines them in a single composite image. The composite fused image can better describe the scene than any of the source images. In this paper, we have proposed a method for noisy image fusion in contourlet domain. The proposed method works equally well for fusion of noise free images. Contourlet transform is a multiscale, multidirectional transform with various aspect ratios. These properties make it more suitable for image fusion than other conventional transforms. In the proposed work, the fusion algorithm is combined with a denoising algorithm to reverse the effect of noise. In the proposed method, we have used a level dependent threshold that is based on standard deviation of contourlet coefficients, mean and median of the absolute contourlet coefficients. Experimental results demonstrate that the proposed method performs well in the presence of different types of noise. Performance of the proposed method is compared with principal components analysis and sharp fusion based methods as well as other fusion methods based on variants of wavelet transform like dual tree complex wavelet transform, discrete wavelet transform, lifting wavelet transform, multiwavelet transform, stationary wavelet transform and pyramid transform using six standard quantitative quality metrics (entropy, standard deviation, edge strength, fusion factor, sharpness and peak signal to noise ratio). The combined qualitative and quantitative evaluation of the experimental results shows that the proposed method performs better than other methods. 相似文献
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在提升算法及小波包变换原理基础上,构造了基于插值细分的小波SGW(6,6);介绍了基于最优提升小波包基分解的阈值去噪算法,将实测爆破振动信号通过二代小波包分解,对小波包系数进行阈值量化,再对阈值处理后的系数进行重构,成功地实现了爆破振动测试信号中的噪声去除。为将二代小波包变换引入到爆破振动效应分析研究领域奠定了基础。 相似文献