红血球图像表面曲率的计算方法

提出一种基于阴影恢复技术的细胞图像表面各点的曲率计算方法,利用边界轮廓提取技术分离由扫描电镜SEM得到的红血球亮度图像,对单个细胞从跟踪得到的中心点开始进行区域生长,其每个像素点的亮度变化信息决定了与之相对应的高度场深度,阴影恢复技术可以将这种变化的亮度转化为不同的深度,从而得到像素的三维数据点。通过最小二乘法对三维点进行曲面拟合得到分段连续光滑的细胞表面,其形状特征由平均曲率与高斯曲率来表示。结果表明,该方法具有很强的实用性和应用价值。     

基于改进型PCNN的不规则图像自适应分割算法研究

针对不规则图像成分复杂、特征形状无法采用经典模型拟合等问题,提出了基于改进型脉冲耦合神经网络（PCNN）的自适应分割算法。该算法在原有PCNN模型基础上,对神经元反馈输入函数和动态阈值函数进行了修正,同时对神经元的输出采用多级输出模型,从而实现对不规则图像的分割。仿真实验表明,改进后的算法能够实现不规则图像的自适应分割,鲁棒性较好。     

自适应不规则形状扩展目标跟踪算法

针对具有不规则形状的扩展目标跟踪问题,在目标先验形状未知和运动过程中目标形状发生突变的情况下,提出基于星凸形随机超曲面模型的自适应轮廓扩展目标跟踪算法.首先,研究星凸形扩展目标形状的径向函数傅里叶系数自适应设定问题,基于中心轮廓距离提出不规则形状的自适应方法.其次,针对扩展目标在运动过程中可能发生的形状突变问题,结合滑动窗口构造检测统计量对目标形变进行实时检测,并提出自适应轮廓快速逼近算法对形状突变的扩展目标进行实时跟踪.然后,提出一种拟Jaccard距离作为形状估计质量的评价指标.最后,仿真实验验证了本文所提算法的有效性.     

一种基于MAS的自适应图像分割方法

复杂图像的全自动分割是极具挑战性的问题．提出了一种基于MAS的自适应图像分割方法．通过属性和行为描述Agent个体，通过合作竞争描述了Agent间的交互．MAS系统在Agent反复自适应过程中达到平衡，同时完成图像分割．通过分割复杂的医学图像证实了该方法的有效性，MAS在图像分割领域具有应用价值．     

一种自适应的多目标图像分割方法

图像分割是数字图像处理中的一个重要问题。提出了一种自适应的多目标图像分割方法。该方法改进了传统的最大类间方差法(OSTU方法)，并使用方差分解的方法自适应地确定图像中的最佳目标个数。采用遗传算法优化了阈值的求取。对样本图像的分割结果显示，此算法在分割速度和效果上都取得了较好的结果。实验证明，此方法能有效地对多目标图像进行分割。     

基于两阶段搜索自适应正交投影分解的图像分割方法

针对应用于图像分割的自适应正交投影分解方法的具体算法的实现问题,提出了一种基于两阶段搜索的实现算法。该算法首先通过粗略搜索得到图像直方图中高斯分量的均值和标准差的大致取值范围,再采用0.618法精确搜索来得到准确的均值;然后将直方图投影到若干合适尺度的基函数上来得到多个匹配值,再利用曲线拟合方法估计出分量的标准差和权值,进而计算出最优阈值,以用于图像分割。理论分析和实验结果表明,基于两阶段搜索自适应正交投影分解的图像分割算法不但计算开销少,而且对图像直方图的描述具有良好的性能,并可以获得较好的图像分割效果。     

基于多尺度曲面拟合的细胞图像表面分割算法

针对不规则多形变形状变化特征的红血球细胞分类识别问题,提出一种基于阴影形状恢复技术与多尺度曲面拟合相结合的复杂表面分割方案.利用Shape from Shading进行重建得到的细胞表面三维高度场可用于计算高斯曲率与平均曲率并得到表面类型图像标识的初始分割,然后通过多尺度二元多项式函数分别对不同类型表面进行曲面拟合,得到的分割结果可将不同形状部分区分开来,并以此作为进一步对细胞进行分类的依据.实验结果表明,该方法易于实现,且分割效果较好.     

基于遗传量子的自适应图像分割算法

虚拟娱乐照相领域的图像分割,目的在于实时地提取显示出背景中的感兴趣目标,但由于常用的分割方法容易丢失图像的边缘细节信息,而且运算时间长,难以实现质量与速度的平衡,为此文章提出了一种基于遗传量子进化算法的图像分割方法。该方法首先运用遗传量子方法进行阈值搜索,然后进行区域分割,再利用形态学方法对图像边缘细节进行补偿,并去除噪声。仿真结果表明,与传统方法比较,该方法能有效地提取图像中的人像,保留细节边缘。     

基于MATLAB的不规则面积图像测量

数字图像测量作为近年来在测量领域新兴的一种高性能测量技术,在日常生活及科研工作中都有其重要的应用。针对许多复杂环境条件下的测量工作,适时性和非接触性的优点更是使得数字图像测量得到很好的运用。本文介绍了一种在非高精度情形下应用MATLAB测量不规则图像面积的方法,并用实例分析证明其有效性。     

二维不规则形状自动排料算法的研究与实现

针对二维不规则形状自动排料问题,给出了解决自动排料的求解思路,并对自动排料的主要算法作出了研究和改进,以便更好地提高算法效率及可靠性。     

Spatio-temporal shape building from image sequences using lateral interaction in accumulative computation

To be able to understand the motion of non-rigid objects, techniques in image processing and computer vision are essential for motion analysis. Lateral interaction in accumulative computation for extracting non-rigid shapes from an image sequence has recently been presented, as well as its application to segmentation from motion. In this paper, we introduce a modified version of the first multi-layer architecture. This version uses the basic parameters of the LIAC model to spatio-temporally build up to the desired extent the shapes of all moving objects present in a sequence of images. The influences of LIAC model parameters are explained in this paper, and we finally show some examples of the usefulness of the model proposed.     

Refining irregular image illumination with concurrent ownership embedding

Multimedia Tools and Applications - During the image archive transformation, artificial operations often lead to the irregular light distribution, which distorting the content. In this article, we...     

Adaptive fairing of digitized point data with discrete curvature

An algorithm for fairing two-dimensional (2D) shape formed by digitised data points is described. The application aims to derive a fair curve from a set of dense and error-filled data points digitised from a complex surface, such that the basic shape information recorded in the original point data is relatively unaffected. The algorithm is an adaptive process in which each cycle consists of several steps. Given a 2D point set, the bad points are identified by analysing the property of their discrete curvatures (D-curvatures) and first-order difference of D-curvatures, in two consecutive fairing stages. The point set is then segmented into single bad point (SBP) segments and multiple bad point (MBP) segments. For each MBP segment, a specially designed energy function is used to identify the bad point to be modified in the current cycle. Each segment is then faired by directly adjusting the geometric position of the worst point. The amount of adjustment in each cycle is kept less than a given shape tolerance. This algorithm is particularly effective in terms of shape preservation when dealing with MBP segments. Case studies are presented that illustrate the efficacy of the developed technique.     

Biological shape analysis by digital curvature

This paper reports the novel application of digital curvature as a feature for morphological characterization and classification of landmark shapes. By inheriting several unique features of the continuous curvature, the digital curvature provides invariance to translations, rotations, local shape deformations, and is easily made tolerant to scaling. In addition, the bending energy, a global shape feature, can be directly estimated from the curvature values. The application of these features to analyse patterns of cranial morphological geographic differentiation in the rodent species Thrichomys apereoides has led to encouraging results, indicating a close correspondence between the geographical and morphological distributions.     

Hierarchical image analysis using irregular tessellations

A novel multiresolution image analysis technique based on hierarchies of irregular tessellations generated in parallel by independent stochastic processes is presented. Like traditional image pyramids these hierarchies are constructed in a number of steps on the order of log(image-size) steps. However, the structure of a hierarchy is adapted to the image content and artifacts of rigid resolution reduction are avoided. Two applications of these techniques are presented: connected component analysis of labeled images and segmentation of gray level images. In labeled images, every connected component is reduced to a separate root, with the adjacency relations among the components also extracted. In gray level images the output is a segmentation of the image into a small number of classes as well as the adjacency graph of the classes     

Adaptive geometry image

We present a novel post-processing utility called adaptive geometry image (AGIM) for global parameterization techniques that can embed a 3D surface onto a rectangular1 domain. This utility first converts a single rectangular parameterization into many different tessellations of square geometry images(GIMs) and then efficiently packs these GIMs into an image called AGIM. Therefore, undersampled regions of the input parameterization can be up-sampled accordingly until the local reconstruction error bound is met. The connectivity of AGIM can be quickly computed and dynamically changed at rendering time. AGIM does not have T-vertices, and therefore no crack is generated between two neighboring GIMs at different tessellations. Experimental results show that AGIM can achieve significant PSNR gain over the input parameterization, AGIM retains the advantages of the original GIM and reduces the reconstruction error present in the original GIM technique. The AGIM is also for global parameterization techniques based on quadrilateral complexes. Using the approximate sampling rates, the PolyCube-based quadrilateral complexes with AGIM can outperform state-of-the-art multichart GIM technique in terms of PSNR.     

Heuristics for two-dimensional knapsack and cutting stock problems with items of irregular shape

In this paper, the two-dimensional cutting/packing problem with items that correspond to simple polygons that may contain holes are studied in which we propose algorithms based on no-fit polygon computation. We present a GRASP based heuristic for the 0/1 version of the knapsack problem, and another heuristic for the unconstrained version of the knapsack problem. This last heuristic is divided in two steps: first it packs items in rectangles and then use the rectangles as items to be packed into the bin. We also solve the cutting stock problem with items of irregular shape, by combining this last heuristic with a column generation algorithm. The algorithms proposed found optimal solutions for several of the tested instances within a reasonable runtime. For some instances, the algorithms obtained solutions with occupancy rates above 90% with relatively fast execution time.     

Adaptive image segmentation with distributed behavior-based agents

Presents an autonomous agent-based image segmentation approach. In this approach, a digital image is viewed as a two-dimensional cellular environment which the agents inhabit and attempt to label homogeneous segments. In so doing, the agents rely on some reactive behaviors such as breeding and diffusion. The agents that are successful in finding the pixels of a specific homogeneous segment will breed offspring agents inside their neighboring regions. Hence, the offspring agents will become likely to find more homogeneous-segment pixels. In the mean time, the unsuccessful agents will be inactivated, without further search in the environment