数字图像的局部分数阶微分增强

为了扩展传统分数阶微分在图像增强处理中微分阶次的范围,改善传统分数阶微分对图像亮度增强不甚理想的问题,提出了一种局部分数阶微分增强图像算法。首先根据局部分数阶微分理论,建立了数字图像的分数阶微分增强分算法。然后构造了新的数字图像分数阶微分增强模板,在该模板中增加亮度控制函数得到边缘、纹理和对比度同时增强的分数阶微分增强算法。实验表明,该方法能扩大分数阶微分在图像增强处理中阶次的范围;不但能很好地增强图像的边缘、纹理和轮廓等信息,又能明显改善图像的对比度和亮度,增强图像的视觉效果优于传统的分数阶微分增强方法的视觉效果。     

将分数阶微分演算引入数字图像处理

为了系统论述图像分数阶微分对纹理细节的增强能力及其侧抑制原理,提出数字图像分数阶微分掩模及其运算规则。论述了分数阶微分的动力学物理意义并推导了分数阶微积分与经典时-频分析之间的关系,分析了在一定条件下二维分数阶微分的可分离性;其次,从信号处理和生物视觉神经模型两个角度提出图像分数阶微分的高斯差感受野模型,并分析其产生的特殊马赫现象;最后,提出并论述了数字图像分数阶微分掩模及其运算规则。计算机数值实验结果表明,对于纹理细节信息丰富的图像信号而言,分数阶微分对灰度变化不大的平滑区域中的纹理细节信息的增强效果明显优于整数阶微分运算。     

基于视觉特性的Riemann-Liouville分数阶图像增强

为了解决Grünwald-Letnikov分数阶微分算法对彩色图像增强容易产生色彩失真以及传统方法增强效果不明显的问题,在分数阶微分视觉模型的基础上提出了一种基于Riemann-Liouville(R-L)分数阶微分的数字图像增强算法。首先讨论了数字图像的分数阶微分视觉模型;接着在R-L分数阶微分方程的基础上构造了8个方向上的分数阶微分增强模板;并讨论了这些微分增强模板的数值运算规则;并在HSI色彩空间对I分量进行分数阶微分实现彩色图像的增强处理。实验表明本文方法具有非线性特性,对图像增强效果明显,且增强     

基于分数阶微分的边缘检测

为了提高图像边缘提取的信噪比,更有效和准确检测图像边缘,由信号的微分特性得出分数阶微分算子较传统1阶和2阶微分算子具有更高的信噪比,然后根据经典的G-L分数阶微分定义推导出的分数阶差分方程,构建了近似的分数阶Tiansi模板.实验证明,基于分数阶微分的边缘提取算子,可以有效提取边缘和有比传统的算子更高的信噪比.     

Riemann-Liouville分数阶图像增强算法及其电路实现

为了解决整数阶微分对图像纹理增强效果不明显及Grümwald-Letnikov（G-L）微分后会使RGB彩色图像边缘色彩失真的问题,提出了一种Riemann-Liouville（R-L）分数阶图像增强算法并讨论了该算法的电路实现.从R-L定义出发,推导出分数阶微分方程,构造了数字图像8个方向上的0~1阶分数阶微分模板并讨论了其数值运算规则,在此基础上构造并实现了数字图像的R-L分数阶微分电路,并在HSI空间对I分量进行分数阶微分实现彩色图像增强.实验结果表明,该算法能比较明显地增强图像的纹理和边缘细节,增强后的图像清晰度和对比度提高,图像视觉效果明显,具有非线性增强灰度图像和彩色图像的复杂纹理特征及边缘信息的独特优势和良好效果.     

基于分水岭变换的浮选泡沫图像分割

针对煤泥浮选泡沫图像中煤泥气泡互相粘连、边界模糊的情况,根据分水岭变换模拟浸水原理,提出一种新的、有效的分割算法,同时应用数学形态学中的腐蚀和膨胀算法,解决了过分割和欠分割问题,使得粘连的煤泥气泡得到了有效的分割。并对分割后的图像进行了物理特征提取,如气泡的数目、颜色、大小分布等,这些特征参数是评价浮选过程好坏的重要信息,利用它们可监测和优化浮选过程。该分割算法成功的用于煤泥浮选泡沫的分割,并且提取出了煤泥泡沫图像所有静态的物理特征参数。     

基于边缘检测和自动种子区域生长的图像分割算法

针对传统区域生长算法对噪声敏感和初始种子过度依赖的问题,提出一种基于边缘提取和自动区域生长相结合的分割算法,即先利用Canny算子对图像进行边缘提取,得到边缘像素点集,再利用灰度直方图从点集中选取自动种子点,结合平均相似度,采用区域生长法进行图像分割。该算法能够实现阈值和种子的自动选取,在传统算法中很难实现。实验结果表明该算法有效。     

基于泡沫表面特征变化及支持向量机的气泡稳定度测量

为提取浮选气泡的动态特性,提出了基于相邻帧气泡区域特征变化检测的气泡动态变化检测与测量方法,并引入支持向量机,将测量问题转化为分类问题.首先分别通过阈值分割法和分水岭实现相邻2帧气泡亮点区域和气泡个体区域的提取,而后根据气泡破裂合并等事件的内在特点提取上述2种区域共6项变化特征,接着完成支持向量机分类器的训练和设计,最终完成气泡稳定度的分类,并通过提取分类器的决策值做进一步的归一化处理后得到气泡稳定度数据.算法在某一铅锌浮选车间进行了实验,结果表明:算法能够实现对浮选气泡所发生的动态事件的检测,并根据特征变化参数对其稳定程度作出分类判决,同时给出定量的气泡稳定度测量值.     

基于谷点边界扫描及区域合并的浮选气泡提取

针对浮选气泡图像分割难的问题,基于多尺度分析、谷点边界扫描及区域合并的思想提出了一种新的气泡图像分割算法.该算法是建立在多尺度分析和谷点边界跟踪的基础之上的,包括图像预处理,有选择的图像缩小,谷点边界扫描,端点检测及联结,区域合并,缩小图象的分割结果映射到原始图像等步骤,算法可以一次性检测出所有谷点,而不用作图像阈值处理,就可以直接得到谷点边界的二值图像.与基于相似性/Canny边界扫描的算法及分水岭等算法做了对比实验,实验结果表明:1)新算法能够较好地克服气泡破裂及其它噪声的影响,减少过分割现象,具有更好的分割精度;2)其运行速度提高了50～100倍,满足实际应用领域的系统实时性要求.     

智能电表条形码图像分割与增强算法研究

为了解决智能电表自动化检测系统中的关键技术问题,即在智能电表图像中自动定位条形码区域并识别出条码信息,通过设计维纳滤波器对图像去噪,利用一阶微分Sobel算法突出条形码区域达到精确分割条形码区域的目的.针对在实际工业环境中拍摄的图像模糊不清问题,提出了改进的提升小波的多分辨率图像增强算法.试验结果表明,该处理方法能够精确定位条形码,且增强后的图像能很好地保持条码的纹理信息,为识别条形码提供了可靠保证.     

Flotation bubble image segmentation based on seed region boundary growing

Segmenting blurred and conglutinated bubbles in a flotation image is done using a new segmentation method based on Seed Region and Boundary Growing (SRBG). Bright pixels located on bubble tops were extracted as the seed regions. Seed boundaries are divided into four curves: left-top, right-top, rightbottom, and left-bottom. Bubbles are segmented from the seed boundary by moving these curves to the bubble boundaries along the corresponding directions. The SRBG method can remove noisy areas and it avoids over- and under-segmentation problems. Each bubble is segmented separately rather than segmenting the entire flotation image. The segmentation results from the SRBG method are more accurate than those from the Watershed algorithm.     

基于气泡跟踪与相位相关的浮选表面气泡平移运动估计

针对浮选表面气泡运动估计难以兼顾准确度和速度的问题,提出了结合气泡跟踪与相位相关的浮选表面气泡运动估计方法。首先对序列的相邻2帧图像按灰度最小值进行尺度缩小,然后通过阈值分割法提取其气泡高亮区域以完成气泡跟踪,最后采用相位相关法得到气泡平移运动信息。同时提出一种分块相位相关法用于进一步获取各个气泡子区域的运动特征。多组运动估计仿真测试证实了算法的有效性和准确度。铅锌浮选厂的现场实验结果表明,算法能快速地获取气泡运动矢量,分块相位相关法能够提供各子块的运动特征。浮选各槽的气泡运动曲线数据表明：每个浮选槽表面气泡的平移运动均表现出了一定的周期性;同一等级的浮选槽里的气泡平移运动曲线和运动紊乱程度曲线具有类似的分布规律;保持一定程度的气泡运动紊乱性有助于提高精矿的品质。     

A method of automatic image segmentation for gas bubbles in water

This paper presents an algorithm of automatic bubble image segmentation using the improved ant colony optimization methodology. The ant colony optimization method is a metaheuristic algorithm, and has been applied in many fields. To reveal the versatility and appropriateness of automatic bubble image segmentation, the fuzzy clustering analysis method is employed in ant colony optimization algorithm. Compared with the well-known image feature extraction operators such as SUSAN and Canny, the proposed method can comparatively suitable to extract the gas bubbles image edge features. The experimental results show that the proposed method is effective and reliable, and can achieve satisfactory image edge extraction effect.     

煤泥浮选泡沫层中气泡特征的提取

利用图象处理的方法来突出浮包沫表面的气泡与图象背景的对比,并提取气泡的边界,为建立泡沫精神与图象中泡沫表面气泡个体统计特征间的关系模型提供依据。在对泡沫表面各种气泡形态的分析中,提出了气泡提取在煤泥浮选泡沫图象分析的可行性。通过对实验室浮选柱所采集的51组泡沫图象进行处理分析,认为图象灰度直方图的均衡化和中值滤波算法对提高图象的动态对比范围,突出气泡的亮度有明显的效果。最后还分析了适合于气泡提取的阈值分割算式法以及气泡的边缘检测算法,从而描述泡沫层图象中气泡的大小和形状等个体特征,达到识别煤泥浮选泡沫图象的目的。     

New two-dimensional fuzzy C-means clustering algorithm for image segmentation

To solve the problem of poor anti-noise performance of the traditional fuzzy C-means （FCM） algorithm in image segmentation, a novel two-dimensional FCM clustering algorithm for image segmentation was proposed. In this method, the image segmentation was converted into an optimization problem. The fitness function containing neighbor information was set up based on the gray information and the neighbor relations between the pixels described by the improved two-dimensional histogram. By making use of the global searching ability of the predator-prey particle swarm optimization, the optimal cluster center could be obtained by iterative optimization, and the image segmentation could be accomplished. The simulation results show that the segmentation accuracy ratio of the proposed method is above 99%. The proposed algorithm has strong anti-noise capability, high clustering accuracy and good segment effect, indicating that it is an effective algorithm for image segmentation.     

Clustering-driven watershed adaptive segmentation of bubble image

In order to extract froth morphological feature, a bubble image adaptive segmentation method was proposed. Considering the image’s low contrast and weak froth edges, froth image was coarsely segmented by using fuzzy c means (FCM) algorithm. Through the attributes of size and shape pattern spectrum, the optimal morphological structuring element was determined. According to the optimal parameters, some image noises were removed with an improved area opening and closing by reconstruction operation, which consist of image regional markers, and the bubbles were finely separated from each other by watershed transform. The experimental results show that the structural element can be determined adaptively by shape and size pattern spectrum, and the froth image is segmented accurately. Compared with other froth image segmentation method, the proposed method achieves much high accuracy, based on which, the bubble size and shape features are extracted effectively.     

基于运动场分离水下图像增强处理方法研究

对于处于气泡等运动信息干扰情况下的水下视频图像,传统的增强手段不能达到令人满意的要求。本文提出了一种在水下视频图像中去除气泡场的方法。采用独立成分分析算法,将序列图像中的背景和运动目标进行分离处理,在灰度域上区分开气泡和其他运动目标并去除气泡,然后,在已经去除了气泡信号的图像中重新赋上其相邻序列的视频图像中相同位置但清晰的像素值,并使用光流法对实验结果进行了分析。结果证明,此方法能够成功的从水下图像中分离出气泡信号,达到了削弱、去除水下图像中气泡场的目的。     

一种改进的C-V水平集遥感图像分割方法

针对遥感图像边界模糊分割难的问题,提出了一种改进的基于小波变换的C-V水平集分割方法提高其分割准确性.该方法首先使用小波变换得到原图像的高频分量,初步定位图像中高频信号的空间位置;然后根据高频分量的幅值及其空间分布,借鉴反锐化掩模法的思想,增强图像高频信号对水平集分割的指导作用,并优化驱动水平集演化的内、外能量及曲线长度约束能量.实验证明,运用该方法对遥感图像的分割结果比传统水平集方法更准确,能有效的利用局部信息提高水平集能量项的有效性和分割结果的准确性.     

Flotation bubble seed image filling algorithm based on boundary point features

Bubble seed image filling is an important prerequisite for the image segmentation of flotation bubble that can be used to improve flotation automatic control. These common image filling algorithms in dealing with complex bubble image exists under-filling and over-filling problems. A new filling algorithm based on boundary point feature and scan lines (PFSL) is proposed in the paper. The filling algorithm describes these boundary points of image objects by means of chain codes. The features of each boundary point, including convex points, concave points, left points and right points, are defined by the point’s entrancing chain code and leaving chain code. The algorithm firstly finds out all double-matched boundary points based on the features of boundary points, and fill image objects by these double-matched boundary points on scan lines. Experimental results of bubble seed image filling show that under-filling and over-filling problem can be eliminated by the proposed algorithm.