基于交互式分割和勾画匹配的颜色迁移

针对经典全局颜色迁移算法在处理具有复杂色彩的自然图像时常出现较多颜色误传的问题,提出一种基于交互式分割与勾画匹配的局部自适应颜色迁移算法。该算法不仅适用于彩色图像间的颜色迁移,而且适用于灰度图着色。算法基本思路是首先分别对两幅图像进行交互式分割,然后对分割后图像的同质景物区域进行勾画匹配,最后在相匹配区域间进行局部颜色迁移。实验结果表明该算法能很好地实现参考图像和目标图像中同质景物内容区域间的颜色迁移,减少颜色误传的现象。结果图像颜色自然度与局部景物一致度有明显提高。     

A multiresolution image segmentation technique based on pyramidalsegmentation and fuzzy clustering

In this paper, an unsupervised image segmentation technique is presented, which combines pyramidal image segmentation with the fuzzy c-means clustering algorithm. Each layer of the pyramid is split into a number of regions by a root labeling technique, and then fuzzy c-means is used to merge the regions of the layer with the highest image resolution. A cluster validity functional is used to find the optimal number of objects automatically. Segmentation of a number of synthetic as well as clinical images is illustrated and two fully automatic segmentation approaches are evaluated, which determine the left ventricular volume (LV) in 140 cardiovascular magnetic resonance (MR) images. First fuzzy c-means is applied without pyramids. In the second approach the regions generated by pyramidal segmentation are merged by fuzzy c-means. The correlation coefficients of manually and automatically defined LV lumen of all 140 and 20 end-diastolic images were equal to 0.86 and 0.79, respectively, when images were segmented with fuzzy c-means alone. These coefficients increased to 0.90 and 0.93 when the pyramidal segmentation was combined with fuzzy c-means. This method can be applied to any dimensional representation and at any resolution level of an image series. The evaluation study shows good performance in detecting LV lumen in MR images.     

基于多参数配准模型的脑核磁影像分割算法

配准技术在基于多图谱的分割方法中能有效地将医学图谱的先验知识融入分割过程,再结合以高效的标记融合算法,最终实现精确地自动分割.针对图谱配准的较大误差及其对标记融合的重要影响,本文建立了一种新的概率图模型框架并以此提出了基于多参数配准模型的分割算法,将此方法与高效的标记融合算法相结合,可以提高目标图像中特定组织区域的分割精度,更使其在少量图谱分割的情形下具有重要应用.首先,使用多种配准参数对所有目标图像进行配准;然后,分别采用不同的算法对配准图像进行灰度融合和标记融合,实现训练图像的重构过程;最后,利用高效的标记融合算法对重构后的图像进行融合得到最终精确的分割结果.实验结果表明该方法均优于本文其他分割算法,能够有效提升脑部组织分割精度.     

A robust automatic clustering scheme for image segmentation usingwavelets

The optimal features with which to discriminate between regions and, thus, segment an image often differ depending on the nature of the image. Many real images are made up of both smooth and textured regions and are best segmented using different features in different areas. A scheme that automatically selects the optimal features for each pixel using wavelet analysis is proposed, leading to a robust segmentation algorithm. An automatic method for determining the optimal number of regions for segmentation is also developed.     

Design and Analysis of an Efficient Evolutionary Image Segmentation Algorithm

Evolutionary image segmentation algorithms have a number of advantages such as continuous contour, non-oversegmentation, and non-thresholds. However, most of the evolutionary image segmentation algorithms suffer from long computation time because the number of encoding parameters is large. In this paper, design and analysis of an efficient evolutionary image segmentation algorithm EISA are proposed. EISA uses a K-means algorithm to split an image into many homogeneous regions, and then uses an intelligent genetic algorithm IGA associated with an effective chromosome encoding method to merge the regions automatically such that the objective of the desired segmentation can be effectively achieved, where IGA is superior to conventional genetic algorithms in solving large parameter optimization problems. High performance of EISA is illustrated in terms of both the evaluation performance and computation time, compared with some current segmentation methods. It is empirically shown that EISA is robust and efficient using nature images with various characteristics.     

彩色图像中文本的定位

随着基于内容检索技术的发展，人们逐渐意识到包含在彩色图像和视频中的文本具有重要的语义作用。提取并识别图像中的文本首先需要确定它们的位置。本文提出了一种新的基于连通区域确定彩色图像中文本位置的方法，它首先对图像进行多通道分解以得到连通区域，然后对连通区域的尺寸和位置排列进行分析以确定图像中文本的位置。本文方法不仅能确定水平排列和垂直排列文本的位置，也能确定按一定倾角排列的文本的位置。实验结果表明，该方法可得到较好的文本定位效果。     

结合文字核心区域和扩展生长的藏文古籍文本行切分

藏文古籍文档图像中相邻文本行之间通常存在黏连和重叠的情况,这使得文本行切分成为一项艰巨的任务.因此,提出了一种结合文字核心区域和扩展生长的藏文古籍文档图像的行切分方法.首先,根据二值藏文古籍文档图像中连通域的面积和真圆度去除非音节点,获得音节点图像.其次,通过水平投影音节点图像和垂直投影二值原图,得到文本行基线所处的范...     

二维场景阴影区域的自动鲁棒分割

基于HSV彩色空间的色调值融合RGB色彩模型中的蓝色分量信息,提出了鲁棒而有效的二维彩色图像阴影区域自动分割方法.根据阴影与非阴影区域间存在色调差异,利用HSV彩色模型,提取可能阴影区域.为消除提取出的阴影区域中偏蓝物体影响,采用RGB彩色空间中的蓝色分量为模板,计算该模板与提取出的阴影区域间的直方图.采用单阈值化分割方法,确定该直方图阈值.将蓝色分量值低于该阈值的阴影区域确定为有效阴影区域.通过对不同光照下的实际自然场景图像的阴影检测,实验结果表明文中所提方法是有效可行的.     

基于可变类FCM算法的多光谱遥感影像分割

为了自动确定多光谱遥感影像中地物目标类别数,该文提出一种基于可变类模糊C均值(Fuzzy C-Means, FCM)的多光谱遥感影像分割方法。首先定义像素与聚类的非相似性测度并据此构建目标函数,而后通过求解目标函数得到最优模糊隶属度和聚类中心。其次,研究模糊因子与影像地物目标类别数的关系,并通过定义划分熵(Partition Entropy, PE)指数优选模糊因子,选择PE指数值稳定收敛后所对应的最小模糊因子值为最优模糊因子,根据模糊因子与类别数的关系得到最优类别数,从而实现了影像的可变类分割。最后,利用提出算法分别对合成和真实多光谱遥感影像进行分割实验,实验结果表明,提出算法不仅能自动确定影像的最优类别数,还能获得较好的分割结果,为实现自动确定遥感影像中地物目标类别数提供新方法。     

一种基于相对模糊连通度的交互式序列图像快速分割算法

在基于相对模糊连通度的交互式图像分割方法的基础上提出了一种序列图像分割的快速算法。对于单幅图像,新算法在保持分割精度的同时,运行速度提高了3倍。作为该算法的扩展,一方面将原算法中单目标分割推广到多目标分割,另一方面将单帧图像的分割推广到序列图像的分割,实现了复杂背景下多目标的图像分割和图像序列的批处理分割。并进行了分割结果的后处理,提取出目标的单像素宽度的光滑边缘。用人造图像和实际的医学图像和图像序列所做的测试实验取得了令人满意的分割结果。     

Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment

Automatic retinal image analysis is emerging as an important screening tool for early detection of eye diseases. Glaucoma is one of the most common causes of blindness. The manual examination of optic disk (OD) is a standard procedure used for detecting glaucoma. In this paper, we present an automatic OD parameterization technique based on segmented OD and cup regions obtained from monocular retinal images. A novel OD segmentation method is proposed which integrates the local image information around each point of interest in multidimensional feature space to provide robustness against variations found in and around the OD region. We also propose a novel cup segmentation method which is based on anatomical evidence such as vessel bends at the cup boundary, considered relevant by glaucoma experts. Bends in a vessel are robustly detected using a region of support concept, which automatically selects the right scale for analysis. A multi-stage strategy is employed to derive a reliable subset of vessel bends called r-bends followed by a local spline fitting to derive the desired cup boundary. The method has been evaluated on 138 images comprising 33 normal and 105 glaucomatous images against three glaucoma experts. The obtained segmentation results show consistency in handling various geometric and photometric variations found across the dataset. The estimation error of the method for vertical cup-to-disk diameter ratio is 0.09/0.08 (mean/standard deviation) while for cup-to-disk area ratio it is 0.12/0.10. Overall, the obtained qualitative and quantitative results show effectiveness in both segmentation and subsequent OD parameterization for glaucoma assessment.     

Intrathoracic airway trees: segmentation and airway morphology analysis from low-dose CT scans

The segmentation of the human airway tree from volumetric computed tomography (CT) images builds an important step for many clinical applications and for physiological studies. Previously proposed algorithms suffer from one or several problems: leaking into the surrounding lung parenchyma, the need for the user to manually adjust parameters, excessive runtime. Low-dose CT scans are increasingly utilized in lung screening studies, but segmenting them with traditional airway segmentation algorithms often yields less than satisfying results. In this paper, a new airway segmentation method based on fuzzy connectivity is presented. Small adaptive regions of interest are used that follow the airway branches as they are segmented. This has several advantages. It makes it possible to detect leaks early and avoid them, the segmentation algorithm can automatically adapt to changing image parameters, and the computing time is kept within moderate values. The new method is robust in the sense that it works on various types of scans (low-dose and regular dose, normal subjects and diseased subjects) without the need for the user to manually adjust any parameters. Comparison with a commonly used region-grow segmentation algorithm shows that the newly proposed method retrieves a significantly higher count of airway branches. A method that conducts accurate cross-sectional airway measurements on airways is presented as an additional processing step. Measurements are conducted in the original gray-level volume. Validation on a phantom shows that subvoxel accuracy is achieved for all airway sizes and airway orientations.     

基于冗余小波变换的相对模糊连通图像交互式分割算法

为了提高图像分割算法的抗噪声性能,该文提出一种基于冗余小波变换的相对模糊连通图像交互式分割算法。与文献中基于尺度的相对模糊连通交互式分割算法相比,新算法在几乎不改变分割效果的前提下,速度提高了十几倍,且对不同的噪声模型均具有较好的鲁棒性。此外,文中还对小波变换的不同尺度对图像分割效果的影响进行了较为系统地研究。用人造图像和实际的医学图像所做的测试实验结果表明,该文提出的新算法可实现对非噪声和噪声图像中感兴趣目标的快速而准确的分割。     

An efficient face segmentation algorithm based on binary partition tree

This paper presents an efficient face segmentation algorithm based on binary partition tree. Skin-like regions are first obtained by integrating the results of pixel classification and watershed segmentation. Facial features are extracted by the techniques of valley detection and entropic thresholding, and are used to refine the skin-like regions. In order to segment the facial regions from the skin-like regions, a novel region merging algorithm is proposed by considering the impact of the common border ratio between adjacent regions, and the binary partition tree is used to represent the whole region merging process. Then the facial likeness of each node in the binary partition tree is evaluated using a set of fuzzy membership functions devised for a number of facial primitives of geometrical, elliptical and facial features. Finally, an efficient algorithm of node selecting in the binary partition tree is proposed for the final face segmentation, which can exactly segment the faces without any underlying assumption. The performance of the proposed face segmentation algorithm is demonstrated by experimental results carried out on a variety of images in different scenarios.     

快速脑白质损伤分割

本文提出了一种快速鲁棒的脑白质损伤分割算法。该算法能可靠地分割白质损伤区域。与大多数基于学习的算法不同,他们大多以专家标记的映射图为基础来学习分类器。本文方法基于如下事实：白质损伤区域的体素相对于正常体素具有较高的亮度。该方法只需要用户标出一些包含白质损伤的兴趣区域（ROI）,然后算法首先执行一个K-means聚类,做初步的白质损伤区域标记。对标记的区域做进一步的学习获得一幅损伤区域的概率图,根据概率图产生一张置信图。最后引入SIFT算法中的多尺度处理的方法。在初始的标签的基础上,将依据邻近的多层切片的概率分布做进一步的标签的确认,最后得到较好的分割结果。     

基于边缘约束的红外目标图像松弛分割技术

本文提出了一种基于边缘约束的、能克服门限面技术和简单的松弛技术不足的红外目标图像松弛分割算法,该方法在确定显著边缘象素的基础上,建立了一个评估在不同初始概率下松弛分割结果的准则函数.通过自动调整初始概率,获得该准则函数的最优或次优解,从而得到满意的分割结果.文中给出了关于真实红外目标图像的分割结果.     

基于模糊C均值聚类的作物病害图像分割方法研究

图像分割是指将人们感兴趣的目标从背景中分割出来,分割结果的好坏直接影响后期的图像分析和识别.基于作物病害图像的分割技术就是将病斑从病害图像中分割出来,以便于后期病害的诊断和识别.模糊C均值聚类是一种重要数据分析和建模的无监督方法,为提高作物病害图像的分割效果,根据作物病害图像的特点,提出一种基于模糊C均值聚类算法的作物病害图像自适应分割方法,并与K均值聚类算法进行比较,结果显示本文算法在进行图像分割方面表现出明显优势.     

Learning bayesian classifiers for scene classification with a visual grammar

A challenging problem in image content extraction and classification is building a system that automatically learns high-level semantic interpretations of images. We describe a Bayesian framework for a visual grammar that aims to reduce the gap between low-level features and high-level user semantics. Our approach includes modeling image pixels using automatic fusion of their spectral, textural, and other ancillary attributes; segmentation of image regions using an iterative split-and-merge algorithm; and representing scenes by decomposing them into prototype regions and modeling the interactions between these regions in terms of their spatial relationships. Naive Bayes classifiers are used in the learning of models for region segmentation and classification using positive and negative examples for user-defined semantic land cover labels. The system also automatically learns representative region groups that can distinguish different scenes and builds visual grammar models. Experiments using Landsat scenes show that the visual grammar enables creation of high-level classes that cannot be modeled by individual pixels or regions. Furthermore, learning of the classifiers requires only a few training examples.     

Rule-based automatic segmentation of color images

A rule-based segmentation algorithm for color images has been presented in this paper. The proposed strategy is similar to region growing algorithm where the seed points are automatically selected and grown. The similarity percents of neighboring pixels are calculated by means of fuzzy reasoning rules, and the merging of the pixels with regions is performed by comparing the similarity percent with the similarity threshold value. The algorithm does not require any prior knowledge of the number of regions existing in the image and decreases the computational load required for the fuzzy c-means (FCM). Several computer simulations have been performed and the results have been discussed. The simulation results indicate that the proposed algorithm yields segmented color image of perfect accuracy.     

Image and texture segmentation using local spectral histograms.

We present a method for segmenting images consisting of texture and nontexture regions based on local spectral histograms. Defined as a vector consisting of marginal distributions of chosen filter responses, local spectral histograms provide a feature statistic for both types of regions. Using local spectral histograms of homogeneous regions, we decompose the segmentation process into three stages. The first is the initial classification stage, where probability models for homogeneous texture and nontexture regions are derived and an initial segmentation result is obtained by classifying local windows. In the second stage, we give an algorithm that iteratively updates the segmentation using the derived probability models. The third is the boundary localization stage, where region boundaries are localized by building refined probability models that are sensitive to spatial patterns in segmented regions. We present segmentation results on texture as well as nontexture images. Our comparison with other methods shows that the proposed method produces more accurate segmentation results.