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病理细胞核的精准分割是病理学诊断的基础,然而当前算法针对带有核分裂象的乳腺癌细胞核自动分割效果差强人意。本文针对当前细胞核分割算法展开分析研究,并提出了一种基于注意力机制和残差结构相结合的U型网络(U_net)用于解决因核分裂象和非核分裂象细胞形态特征十分接近造成细胞核分割精度不足的问题。通过实验表明本文提出算法的平均像素准确率(mean pixel accuracy,MPA)和Mean_dice指标系数分别为0.74和0.82。与原有算法相比,训练指标分别提升了11%和9%,证明本文算法的可行性。 相似文献
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白细胞在人体血液中起着至关重要的作用,白细胞的自动分割和识别是计算机图像处理和模式识别在医学图像领域应用的一个重要研究课题.针对各种白细胞的分类识别研究,提出一种实时性、鲁棒性较好的算法.该算法主要利用支持向量机SVM对细胞图像进行分割处理,并与其他两种经典方法进行比较,得出了较好的结果. 相似文献
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字幕分割是指对检测定位到的视频字幕图像进行分割,使其字符像素与本底背景像素分离,二值化为可供OCR软件识别的字幕图像。为了克服字幕图像分割中容易出现的过分割及欠分割现象,提出一种基于字幕区域和外扩区域"白像素"数量增量比判决的字幕图像分割算法,该算法通过逐步改变图像分割阈值,以分析图像分割结果作为反馈来判决当前分割效果的好坏,从而确定最优分割阈值。大量结果表明,该算法性能良好,其分割效果远优于传统的OTSU算法、K均值聚类等经典算法。 相似文献
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x射线BGA焊点检测图像中,存在大量与检测无关的冗余信息。针对这一问题,从原理和应用效果上对三种基本阈值分割算法进行了对比分析并进行仿真实验。通过对三种分割算法的比较,得到了较为通用的算法,效果令人满意。 相似文献
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SAR图像基于Rayleigh分布假设的最小误差阈值化分割 总被引:6,自引:0,他引:6
针对合成孔径雷达(SAR)图像的特点,本文提出基于灰度直方图的混合偏移Rayleigh分布假设下的最小误差阈值化分割算法,并与现有的基于Gauss和Poisson分布假设下的最小误差分割算法以及经典的Otsu算法作了比较。实验和Kolmogorov-Smirnov检验结果表明对SAR图像而言,基于Rayleigh假设的算法可以取得更好的分割效果。 相似文献
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基于自适应阈值分割的宫颈细胞图像分类算法 总被引:3,自引:0,他引:3
本文以宫颈癌细胞图像的自动筛查为应用背景,研究了一种新的宫颈细胞图像分类算法。算法首先采用形态学滤波与自适应直方图均衡的预处理方法进行图像增强;根据对图像内容与直方图分布关系的深入分析,提出采用经验因子加权Otsu自适应阈值分割算法进行细胞核分割,有效地解决了细胞重叠所引起的自适应分割阈值的选取问题;然后,通过提取面积、周长、区域面积与外接凸多边形面积比以及长宽比四种参数,对分割出的细胞核区域进行杂质剔除;最后以最能体现癌细胞特征的面积、平均灰度作为特征参数采用K-means算法对样本图像进行分类实验。实验样本为233幅宫颈细胞图像,其中49幅癌细胞图像,184幅正常细胞图像,实验结果证明了该算法的有效性。 相似文献
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为了获得实用、可靠的植物识别方法,必须在识别植物过程中进行图像分割。本文针对具有明显颜色优势的图像,采用Otsu阀值算法及边缘检测分割算法,对图像进行处理,并对其结果进行对比分析和优劣比较。得出结论:对具有明显颜色优势的图像,采用边缘检测分割算法的应用效果较好;对比边缘检测算法中常用的Prewitt算子、Roberts算子和Sobel算子的输出效果,认为Sobel算子不仅能输出较为理想的效果,同时还可大幅度减少运算时间。 相似文献
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This paper presents a robust and computationally efficient genetic algorithm for color classification. It designs well-fitted color space prolate spheroids (ellipsoids) that envelop the training pixels. The ellipsoids are then used to classify unlabeled image pixels in accordance with their color, in order to partition the image. The color classification algorithm described here has very low error rates, boasts very high operational speed, and permits trading higher indecision rates for lower rates of misclassification. The performance of the color classifier developed in this paper is compared with those of the support vector machine (SVM) and the nearest-neighbor (kNN) classifiers. It has been shown that our color classifier outperforms SVM and kNN for partitioning of color images that contain several closely spaced color classes. It has higher correct classification, lower misclassification, and significantly reduced operational latency in comparison with color classifiers based on kNN and SVM. 相似文献
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研究了一种基于支持向量机的飞机图像识别算法。采用基于神经网络的图像边缘检测方法,该方法首先基于邻域灰度极值提取边界候选图像,然后以边界候选象素及其邻域象素的二值模式作为样本集,输入边缘检测神经网络进行训练。提取具有RST不变性的轮廓特征向量,输入支持向量机进行训练和识别。将支持向量机与传统的人工神经网络的算法进行了对比实验,实验表明基于支持向量机的飞机图像识别算法具有更好的性能。 相似文献
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提出了一种对二值化后的车牌进行反色判断的新方法.该方法通过计算车牌2~6字符区域的白像素比例和垂直投影的白像素比例,判断车牌是否需要进行反色处理.实验表明,该方法有较高的准确率. 相似文献
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A new method for detecting and suppressing impulsive noise in color images is presented in this paper. The proposed method is a type of switching vector filters, where the impulse detection is based on the order-statistic information about the color samples in the horizontal, vertical, and diagonal directions. The new solution first uses quaternion-based representation of color differences and median deviation-based techniques to search for the edge direction with the maximum number of similar pixels, and then utilizes the samples aligning with this edge direction to judge whether the current pixel is noisy or not and control the switching between identity (no filtering) and vector median filtering actions. Extensive experimental comparisons exhibit the validity of the proposed approach by showing significant performance improvements over other well-known color image filtering techniques. 相似文献
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针对标准支持向量机(SVM)识别非均衡数据往往会出现最优超平面倾向性和正类样本大量错分的现象,探讨SVM识别非均衡数据失效的原因及对策;考虑到SVM最优超平面仅由少量支持向量完全决定的特性,提出一种基于负类边界样本裁剪策略的SVM数学模型.鉴于该模型需经多次负类数据的"训练-裁剪"过程才能较好地识别正类样本且较为费时,以等效的一次性裁掉更多样本的裁截面技术作为替代,提出一种耦合负类样本裁剪与非对称错分惩罚的非均衡SVM算法,并利用改进正余弦优化算法优化裁剪偏移量以提高算法的非均衡数据处理能力.数值实验结果验证了裁剪偏移量的优化必要性、改进正余弦优化算法的较强优化性能和改进SVM算法对非均衡数据的较好识别性能. 相似文献
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Kwanghoon Sohn Kyu-Cheol Lee Jungeun Lim 《Circuits, Systems, and Signal Processing》2001,20(6):643-654
This paper is an enhancement to our earlier research with grey-scale images. In this paper, we propose two new detection-estimation based image filtering algorithms that effectively remove corrupted pixels with impulsive noise in digital color images. The existing methods for enhancing corrupted color images typically possess inherent problems in computation time and smoothing out edges because all pixels are filtered. Our proposed algorithms first classify corrupted pixels in each channel or in each pixel. Because marginal or vector median filtering is only performed for the classified pixels, the process is computationally efficient, and edges are preserved well. In addition, because there is no appropriate criterion to evaluate the performance of impulsive noise detectors for color images, the objective comparison of noise detectors is difficult. Thus, we introduce a new efficiency factor for comparing the performance of noise detectors in digital color images. Simulation results show that the proposed algorithms perform better than existing methods, in both objective and subjective evaluations.This work was supported by the Korea Science & Engineering Foundation (KOSEF) under grant no. 981-0912-057-2. 相似文献
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The generic Meanshift is susceptible to interference of background pixels with the target pixels in the kernel of the reference model, which compromises the tracking performance. In this paper, we enhance the target color feature by attenuating the background color within the kernel through enlarging the pixel weightings which map to the pixels on the target. This way, the background pixel interference is largely suppressed in the color histogram in the course of constructing the target reference model. In addition, the proposed method also reduces the number of Meanshift iterations, which speeds up the algorithmic convergence. The two tests validate the proposed approach with improved tracking robustness on real-world video sequences. 相似文献
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In most digital cameras, Bayer color filter array (CFA) images are captured and demosaicing is generally carried out before compression. Recently, it was found that compression-first schemes outperform the conventional demosaicing-first schemes in terms of output image quality. An efficient prediction-based lossless compression scheme for Bayer CFA images is proposed in this paper. It exploits a context matching technique to rank the neighboring pixels when predicting a pixel, an adaptive color difference estimation scheme to remove the color spectral redundancy when handling red and blue samples, and an adaptive codeword generation technique to adjust the divisor of Rice code for encoding the prediction residues. Simulation results show that the proposed compression scheme can achieve a better compression performance than conventional lossless CFA image coding schemes. 相似文献
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为了在滤除椒盐噪声时更好的保护图像特征信息 ,利用分数阶积分算子、梯度信息和SVM 设计了一种滤波方法 FG-SVM。先设计PCNN噪点检测模型,将检测的噪点及信号点对应位置分别标记为1和0, 生成标记图像;然后根据标 记图像,在噪声图像上对每一个以信号点为中心的5×5区域,用中心 点周围的像素灰度信息、分数阶积分算子及梯度信 息构建训练样本,训练SVM获得去噪模型;再取以噪点为中心的5×5 区域构建测试样本,作为SVM去噪模型的输入来 估计区域中心的灰度值;最后用SVM的估计值取代噪点的灰度值,得到去噪图像。仿真试验 表明,分数阶积分阶次取 1.7时,能获得最好的去噪效果。对含噪 1%的Lena、Pepper及Camer.去噪,FG-SVM 的PSNR比MPCNN分别提高了[4.19,3.64]dB,且去噪图像的边缘细节清晰。 相似文献