数字图像处理在定量金相分析中的应用

提出通过金相图像的数字化处理对材料组织进行定量分析的方法，包括金相图像的采集，图像的分割，特征提取及在此基础上对金属与合金组织特征参数进行测量等，建立了平衡态微观组织的一种检测系统，并以实验检证此方法的可行性。     

小目标玻壳缺陷检测中图像分割问题的研究

对于目标所占比例很小的图像，目标所代表的峰在直方图中呈现为背景峰边上的水平线或微斜线，用传统的分割方法难以确定分割的阈值。针对这种情况，本文提出了一种自适应的局部阈值分割图像的方法，该方法在玻壳缺陷图像的分割中取得了很好的效果。     

基于色彩特征的超声医学图像分析方法

图像分割是计算机图像识别和理解的基础，本文提出一种基于色彩特征的彩色多普勒图像分割和基于频域双线性插值的图像旋转与用户交互式剪切相结合的图像分析方法，通过计算彩色超声医学图像的三基色R，G，B的色彩特征，提取出感兴趣的区域并实现了图像的分割，实验证明这是快速可行的彩色分割方法。     

红外图像的动态阈值分割

在深入探讨目前图像分割算法的基础上，针对实际红外图像的分割提出了一种新的方法－－动态阈值分割算法。它利用局部空间阈值适度调整全局统计最优阈值，因而其分割阈值是动态的。实验表明该算法能够突出图像中感兴趣的细节，单动态阈值的分割效果相当于常规固定双域值的分割。     

医学图像中细胞分割方法简介

细胞分割作为细胞跟踪、细胞分裂检测过程中提取细胞特征的一个重要手段,在医疗图像处理、分析领域占据重要的地位。本文通过对国内外常见生物医学细胞图像分割方法的大量研究,较为全面地介绍了现行的各种细胞分割方法,同时对各个方法的优缺点进行了论述,最后对细胞分割算法进行了归纳总结。     

一种改进的Graph Cuts交互图像分割方法

本文提出了一种改进的Graph Cuts交互图像分割方法。Graph Cuts交互图像分割方法首先由用户选定部分像素作为对象和背景，其余像素为未知区域；然后根据以像素为顶点，以像素相邻关系为边，构造一个图；最后通过图的最小分割方法将图像分为对象和背景两部分。此方法分割图像的结果直接受到用户选定对象和背景像素操作的影响，对象和背景边界的像素容易被分割错误。我们分别对对象区域和背景区域进行腐蚀操作，使分割错误的像素重新变为未知区域（对于在对象或背景内部被错误划分的像素，可以利用类似画笔的工具，直接将其标为对像或背景），然后重新进行一次Graph Cuts分割。由于这次选定了大部分的对象和背景区域，实验结果表明，最后分割结果正确率明显提高了。     

层次式图切分快速分割算法

图切分(Graph Cuts,GC)是近年来兴起的基于图论框架的图像分割方法,该理论的新颖之处在于它的全局最优性和结合多种知识的统一性。但当图像较大时运算非常耗时。该文提出了一种基于GC的层次式图像分割方法。先在低分辨率中用GC以较低的分割代价获取粗尺度的初始分割,再将结果轮廓映射回高分辨率图像中并构造出窄带,进而采用matting思想,在窄带内获取精确分割。实验结果表明,本文方法在确保分割结果准确性的同时,运算速度大幅度提高。     

改进的模糊阈值图像分割方法

提出了一种自适应的模糊阈值图像分割方法,通过预分割和直方图信息相结合的方法,解决了传统的模糊闽值图像分割法难以自动获取窗宽的困难;并针对模糊闽值图像分割方法不能适用于直方图呈单峰分布的图像的缺陷,提出了一个新的平滑迭代公式。该平滑迭代公式利用像素点的邻域信息使图像增强,再使用自适应的模糊阈值图像分割方法进行分割,可以拓宽模糊阈值图像分割方法的适用范围。实验结果表明,使用该方法的目标分割正确率达97.3％,显示了较高的分割精度和较强的鲁棒性。     

遗传算法和最大方差法在图像分割中的应用

图像分割是图像处理中的重要问题．也是计算机视觉研究中的经典难题。文章首先介绍了最大类间方差法，并结合遗传算法的快速寻优的特点，提出了一种利用最大方差法和改进的遗传算法相结合的图像分割的新方法。实验仿真结果表明，该方法可以有效地提高图像分割的计算速度，大大缩短寻找最优阈值的时间，提高图像处理的实时性。     

预扫描算法求解图像分割的Chan-Vese模型(英文)

本文提出了一种新的有效的算法来求解图像分割中的Chan-Vese模型。新算法避免了求解PDE的过程,极大地提高了图像分割的运算速度。这种算法保持了C-V模型和水平集方法的优点,能够自动处理图像分割过程中边缘的拓扑变形,保持边缘的尖角以及对于非凸边缘的有效的检测等等。这种算法思路简单,很容易推广到任意有限维的图像分割问题的求解中。     

Brightness preserving optimized weighted bi-histogram equalization algorithm and its application to MR brain image segmentation

Medical image segmentation is crucial for neuroscience research and computer-aided diagnosis. However, intensity inhomogeneity and existence of noise in magnetic resonance images lead to incorrect segmentation. In this article, an effective method called enhanced fuzzy level set algorithm is presented to segment the white matter, gray matter, and cerebrospinal fluid automatically in contrast-enhanced brain images. In this method, first, exposure threshold is computed to divide the input histogram into two sub-histograms of different gray levels. The input histogram is clipped using a mean gray level to control the excessive enhancement rate. Then, these two sub-histograms are modified and equalized independently to get a better contrast enhanced image. Finally, an enhanced fuzzy level set algorithm is employed to facilitate image segmentation. The extensive experimental results proved the outstanding performance of the proposed algorithm compared with other existing methods. The results conform its effectiveness for MR brain image segmentation.     

一种基于马尔可夫随机场的运动目标检测算法

本文提出了一种基于马尔可夫随机场的运动目标检测算法.针对传统时间分割使用主观固定阁值的缺点,使用马尔可夫随机场模型对差分图像建模,并提出了一种新的模型阶次选择算法,以及一种可以加速收敛过程的随机场迭代算法.采用期望最大算法获取高斯分布参数并检测运动变化区域,利用形态学运算修正时间分割模板;空间分割部分提出了基于人眼视觉特征的改进分水岭算法,有效地解决了过分割问题;最后对时、空间分割结果进行信息融合处理,从而得到完整的运动目标.仿真实验结果证明了本文算法可以有效地分割视频运动目标.     

Automatic brain tissue segmentation in MR images using hybrid atlas forest based on confidence-weighted probability matrix

The segmentation of specific tissues in an MR brain image for quantitative analysis can assist the disease diagnosis and medical research. Therefore, a robust and accurate method for automatic segmentation is necessary. Atlas-based-method is a common and effective method of automatic segmentation where an atlas refers to a pair of image consist of an intensity image and its corresponding label image. Apart from the general multi-atlas-based methods, which propagate labels through the single atlas then fuse them, we proposed a hybrid atlas forest based on confidence-weighted probability matrix to consider the atlases set as a whole and treat each voxel differently. In the framework, we first register the atlas to the image space of target and calculate the confidence of voxels in the registered atlas. Then, a confidence-weighted probability matrix is generated and it augments to the intensity image of the atlas or target for providing spatial information of the target tissue. Third, a hybrid atlas forest is trained to gather the features and correlation information among the atlases in the dataset. Finally, the segmentation of the target tissues is predicted by the trained hybrid atlas forest. The segment performance and the components efficiency of the proposed method are evaluated on the two public datasets. Based on the experiment results and quantitative comparisons, our method can gather spatial information and correlation among the atlases to obtain an accurate segmentation.     

Block-based tri-channel hybrid segmentation of images for foreground extraction

Image segmentation is crucial in image analysis, object representation, visualization and other image processing tasks. An image can be distinguished in terms of the foreground and the background. A new hybrid segmentation of images for foreground extraction is proposed, based on Interval Neutrosophic Set (INS) and Sparse Field Active Contour. In this method, an image is represented in three channels using a Gaussian filter bank and each channel is split into blocks to which the INS is applied. The resultant neutrosophic image for three channels undergoes isodata thresholding to obtain the tri-channel edge image, which is segmented using the Sparse Field Active Contour. The proposed method is evaluated by conducting three different experiments in natural image datasets like the Semantic Dataset100, Weizmann_Seg_DB_1obj, BSR and standard MATLAB test images. Finally, it is compared to other existing segmentation methods, which shows promising achievement in terms of their evaluation metrics like overlap-based metrics, pair-counting-based method and distance measures.     

Image analysis and mathematical morphology for civil engineering materials

The scope of this paper is to present the main tools of image analysis to investigate materials and, specially, civil engineering ones. First the acquisition methods are described. The different operators for filtering, segmentation and binary image processing are presented and illustrated on different images. The influence of the observation field on these operators and the bias correction is also introduced. Then the problem of the parametrical characterization is presented: stereological parameters and functions related to size distributions, dispersion and anisotropy. Finally, the model methods based on image analysis are recalled. Some annexes illustrate this paper to precise main basic notions to understand the morphological tools.     

基于双门限阈值的爆破块度图像识别研究

为改进爆破块度图像分割中容易存在的过分割、噪声、"黑洞"现象等问题,提出了双门限阈值分割技术,开展了多种类型的爆破岩块图像分割实验和小型爆堆的手工测量实验,实验结果表明,双门限阈值法与灰度阈值法、适应性阈值法、Otsu大津法等常用的分割方法相比,对爆破岩堆的图像分割效果更优,能够解决岩石表面噪声问题;与手工测量相比,双门限阈值技术对爆堆图像分割的平均相对误差为12.0%,误差主要因岩块的边缘棱角效应所致,这种效应使得分割结果偏大。双门限阈值分割技术能较好地应用于爆破块度图像识别。     

基于双门限阈值的爆破块度图像识别研究

为改进爆破块度图像分割中容易存在的过分割、噪声、"黑洞"现象等问题,提出了双门限阈值分割技术,开展了多种类型的爆破岩块图像分割实验和小型爆堆的手工测量实验,实验结果表明,双门限阈值法与灰度阈值法、适应性阈值法、Otsu大津法等常用的分割方法相比,对爆破岩堆的图像分割效果更优,能够解决岩石表面噪声问题;与手工测量相比,双门限阈值技术对爆堆图像分割的平均相对误差为12.0%,误差主要因岩块的边缘棱角效应所致,这种效应使得分割结果偏大。双门限阈值分割技术能较好地应用于爆破块度图像识别。     

快速背景重建的在线运动目标检测

为了能快速地从视频图像序列中创建可靠的背景图像,进而提取运动目标,文中提出了一种基于反馈信息的运动目标检测算法.首先提出了基于相邻帧信息和背景估计信息相融合的背景重建算法,保证了在视频场景改变时仍能迅速捕捉背景;还提出了基于一种在线Otsu法的运动目标检测,将相邻帧运动目标信息反馈到目标提取算法中,弥补传统Otsu法的不足;最后提出了对光线变化具有一定鲁棒性的背景估计算法.实验表明,该方法的重建速度快,准确率高,能满足实时检测的需要.     

Coverless Image Steganography Based on Image Segmentation

To resist the risk of the stego-image being maliciously altered during transmission, we propose a coverless image steganography method based on image segmentation. Most existing coverless steganography methods are based on whole feature mapping, which has poor robustness when facing geometric attacks, because the contents in the image are easy to lost. To solve this problem, we use ResNet to extract semantic features, and segment the object areas from the image through Mask RCNN for information hiding. These selected object areas have ethical structural integrity and are not located in the visual center of the image, reducing the information loss of malicious attacks. Then, these object areas will be binarized to generate hash sequences for information mapping. In transmission, only a set of stego-images unrelated to the secret information are transmitted, so it can fundamentally resist steganalysis. At the same time, since both Mask RCNN and ResNet have excellent robustness, pre-training the model through supervised learning can achieve good performance. The robust hash algorithm can also resist attacks during transmission. Although image segmentation will reduce the capacity, multiple object areas can be extracted from an image to ensure the capacity to a certain extent. Experimental results show that compared with other coverless image steganography methods, our method is more robust when facing geometric attacks.     

一种改进的倾斜航拍图像注释块分割方法

航拍图像的注释信息中记录着航拍中重要的成像参数,如相机类型及外方位元素等。本文针对当前倾斜航拍图像注释块分割存在的一系列问题,提出采用小波变换进行分割的改进方法,并结合数学形态学的方法进一步分割出注释块。实验证明,该方法不仅快速,而且准确,取得了良好的分割效果。