基于遗传算法的SAR图像多尺度分割

基于最大期望（EM）算法与遗传算法（GA）,提出一种有效的多尺度SAR图像无监督分割方法。该方法首先利用混合多尺度自回归（MMAR）模型描述SAR图像中由于雷达斑点所引起的不同尺度和同一尺度内像素之间的统计相依性; 然后将GA与EM结合给出MMAR模型的参数估计算法。这种算法利用最小描述长度（MDL）准则,能够选择模型的分量数;最后利用Bayes分类器实现图像的分割。该方法集遗传算法和EM算法的优点,对初始值有较少的敏感性,避免局部最优解,提高了分割精度。实验结果表明GA EM方法优于EM算法。     

基于区域的多尺度全色遥感图像分割

针对边缘信息不足导致的图像误分割问题,提出一种基于区域的多尺度全色遥感图像分割方法.首先,利用曲波变换对图像进行多尺度分析,获取多尺度分解图像;然后,利用规则划分技术将其图像域划分成一系列子块,结合统计方法,建立基于区域的多尺度统计分割模型;接着设计可逆变马尔可夫链蒙特卡罗(RJMCMC)算法求解该分割模型;最后,利用所提出方法对全色遥感图像进行分割实验,实验结果表明,所提出方法能够有效解决图像误分割问题,并较好地实现图像分割.     

基于多尺度继承性SAR图像分割算法

针对高分辨率SAR图像的分割问题,提出一种基于多尺度继承性的分割算法。该算法综合利用图像的宏观和微观特征,将传统的单尺度信息处理技术纳入尺度不断变化的动态分析框架中,更容易获得图像的本质特征。同时,使用异性扩散方程获得多尺度图像序列,采用一种由粗尺度到细尺度的分割策略,先进行粗尺度分割,然后以此分割结果来引导较细尺度层的分割。分割过程中采用迭代自组织的数据分析算法自适应地确定每一层分割的区域个数,较好地建立尺度之间的分割继承关系。该分割算法可以满足不同图像处理任务的需求,也更加符合人的认知过程和视觉处理系     

A multiscale gradient algorithm for image segmentation using watershelds

Watershed transformation is a powerful tool for image segmentation. However, the effectiveness of the image segmentation methods based on watershed transformation is limited by the quality of the gradient image used in the methods. In this paper we present a multiscale algorithm for computing gradient images, with effective handling of both step and blurred edges. We also present an algorithm for eliminating irrelevant minima in the resulting gradient images. Experimental results indicate that watershed transformation with the algorithms proposed in this paper produces meaningful segmentations, even without a region merging step. The proposed algorithms can efficiently improve segmentation accuracy and significantly reduce the computational cost of watershed-based image segmentation methods.     

基于Wedgelet的SAR图像多尺度快速分割方法*

为了解决基于Wedgelet变换的多尺度分割算法在楔形方向的选择上需要计算所有分解楔形系数,且没有利用上层分解的结果,计算量特别大的问题,提出一种从图像的几何结构出发,在图像四分树的基础上加以楔形区域分割,在矩形区域的楔形方向选取上建立多分辨分析的算法.该算法在上层分割的基础上,只需计算八个方向的Wedgelet,而不是所有方向,既避免了窗口初始化,降低了分割过程特征抽取的复杂性,又减少了迭代次数.经试验比较,本文方法优于同类方法.     

基于多尺度颜色小波纹理特征的火焰图像分割

火焰图像分割质量对基于数字成像的燃烧监测十分重要。受炉膛背景及燃烧工况的影响,难以同时满足火焰图像分割速度和准确度（即火焰图像分割结果与真实火焰接近程度）的需求。提出一种基于多尺度颜色特征和小波纹理特征（MCWT）的无监督火焰图像分割方法,用于提高火焰图像分割的质量和速度。结合火焰图像颜色特征及小波纹理特征构建特征矩阵,对特征矩阵进行压缩并初步检测压缩尺度火焰区域。根据压缩尺度火焰边缘确定原始尺度火焰边缘区域并构建火焰边缘区域特征矩阵,进一步分割得到准确火焰图像分割结果。采用该方法对某工业煤燃烧实验炉内不同燃烧工况下的火焰图像进行分割,并与传统分割方法对比。实验结果表明与其他传统分割方法相比,提出方法能够更准确且快速地实现不同燃烧工况下火焰图像的分割,并且其对于含有高斯噪声和椒盐噪声的火焰图像都具有更好的分割效果。     

基于多尺度随机模型的SAR图像无监督分割

合成孔径雷达（synthetic aperture radar,SAR）是一种基于相干原理的成像系统,在SAR图像中存在严重影响图像质量的斑点噪声,使得SAR图像的可靠分割非常困难。〖BP）〗根据SAR图像的成像机理,利用两种多尺度随机模型,即多尺度自回归（Multiscale Autoregressive,MAR）模型和多尺度自回归滑动平均（Multiscale Aautoregressive Moving Average, MARMA）模型,分别来描述同一场景不同分辨率SAR图像像素间的统计相关性,并构造了相应的多分辨混合算法实现SAR图像的无监督分割。试验结果表明,提出的两种无监督分割方法是可行的,且MARMA模型比MAR模型能够更精确地捕捉SAR图像多尺度序列中不同类型地形的统计信息,使分割质量具有明显的改进。     

A Multiscale Approach to Automatic Medical Image Segmentation Using Self-Organizing Map

In this paper,a new medical image classification scheme is proposed using selforganizing map(SOM)combined with multiscale technique.It addresses the problem of the handling of edge pixels in the traditional multiscale SOM classifiers.First,to solve the difficulty in manual selection of edge pixels,a multiscale edge detection algorithm based on wavelet transform is proposed.Edge pixels detected are then selected into the training set as a new class and a multiscale SOM classifier is trained using this training set.In this new scheme,the SOM classifier can perform both the classification on the entire image and the edge detection simultaneously.On the other hand,the misclassification of the traditional multiscale SOM classifier in regions near edges is graeatly reduced and the correct classification is improved at the same time.     

基于多尺度分析与图割的快速图像分割算法*

以GrabCut算法为基础,引入多尺度分析方法,以塔式分解的多尺度图像序列代替固定尺度的原始图像序列估计GMM参数,将粗糙尺度的易分割性与精细尺度的精确性互补,使得算法以较少样本快速确定GMM参数,分割精度不减而效率显著提高。实验表明了算法的有效可行性。     

A multiscale regularity measure as a geometric criterion for image segmentation

Recent results from human vision experiments show that lines of low fractal dimension are highly capable of evoking indification with nameable objects. In other words, regular lines are recognized in human vision as object edges. In this paper, a regularity measure of discrete line geometry is presented. This quantitative measure based on a ratio between lines of varying lengths is analyzed in the framework of brownian motion theory. The measure on a given scale is always computed from the maximum precision image, so that no subresolution assumption is introduced. A choice of scale determines the quantity of global information versus local information one wants to measure. We show how this quantitative measure leads to relevant shape information. To illustrate this, an example of an image segmentation application is realized. The segmentation based essentially on geometry criteria, uses a region-growing process. The process depends on a single parameter that can be fixed in a natural way, comparing contour regularity to a geometric model regularity. We present experimental results performed on real-scene images, including indoor and outdoor images.     

基于自适应多尺度焊缝X光图像缺陷分割研究

焊缝X光图像中的缺陷包括近似圆形和近似条形缺陷。由多尺度边缘检测理论可知：在适当尺度下,圆形缺陷在相互正交的两个方向上都是屋脊边缘,而条形缺陷只在一个方向上是屋脊边缘。利用区域一致性测度自适应确定小波滤波尺度。并在缺陷存在区域自适应确定LOG算子和方向可调滤波器的滤波尺度用以分割两种缺陷。理论分析和实验结果表明,算法有较好的分割效果。     

多尺度超像素分割和奇异谱分析的高光谱影像分类

目的 高光谱影像（hyperspectral image,HSI）中“同物异谱,异物同谱”的现象普遍存在,使分类结果存在严重的椒盐噪声问题。HSI中的空间地物结构复杂多样,单一尺度的空间特征提取方法无法有效地表达地物类间差异和区分地物边界。有效解决光谱混淆和空间尺度问题是提高分类精度的关键。方法 结合多尺度超像素和奇异谱分析,提出一种新的高光谱影像分类方法,从而充分挖掘地物的局部空间特征和光谱特征,解决空间尺度和光谱混淆的问题,提高分类精度。利用多尺度超像素对影像进行分割,获取不同尺度的分割影像,同时在分割区域内进行均值滤波,减少类内的光谱差异,增强类间的光谱差异;对每个区域计算平均光谱向量,并利用奇异谱分析方法获取光谱的主要鉴别特征,同时消除噪声的影响;利用支持向量机对不同尺度超像素分割影像进行分类,并进行决策融合,得到最终的分类结果。结果 实验选取了两个标准高光谱数据集和一个真实数据集,结果表明,利用本文算法提取的光谱—空间特征进行分类,比直接在原始数据上进行分类分别提高约26.8%、9.2%和13%的精度;与先进的深度学习SSRN （spectral-spatial residual network）算法相比,本文算法在精度上分别提升约5.2%、0.7%和4%,并且运行时间仅为前者的18.3%、45.4%和62.1%,处理效率更高。此外,在训练样本有限的情况下,两个标准数据集的样本分别为1%和0.2%时,本文算法均能取得87%以上的分类精度。结论 针对高光谱影像分类中的难题,提出一种新的融合光谱和多尺度空间特征的HSI分类方法。实验结果表明,本文方法优于对比方法,可以产生更精细的分类结果。     

Probabilistic time-series segmentation

Among the major challenges in the realization of practical health monitoring systems is the identification of short-duration events from larger signals. Time-series segmentation refers to the challenge of subdividing a continuous stream of data into discrete windows, which are individually processed using statistical classifiers to recognize various activities or events. In this paper, we propose a probabilistic algorithm for segmenting time-series signals, in which window boundaries are dynamically adjusted when the probability of correct classification is low. Our proposed scheme is benchmarked using an audio-based nutrition-monitoring case-study. Our evaluation shows that the algorithm improves the number of correctly classified instances from a baseline of 75%–94% using the RandomForest classifier.     

Unsupervised multiscale segmentation of color images

This paper proposes a new multiresolution technique for color image representation and segmentation, particularly suited for noisy images. A decimated wavelet transform is initially applied to each color channel of the image, and a multiresolution representation is built up to a selected scale 2^J. Color gradient magnitudes are computed at the coarsest scale 2^J, and an adaptive threshold is used to remove spurious responses. An initial segmentation is then computed by applying the watershed transform to thresholded magnitudes, and this initial segmentation is projected to finer resolutions using inverse wavelet transforms and contour refinements, until the full resolution 2⁰ is achieved. Finally, a region merging technique is applied to combine adjacent regions with similar colors. Experimental results show that the proposed technique produces results comparable to other state-of-the-art algorithms for natural images, and performs better for noisy images.     

A clustering algorithm with optimized multiscale spatial texture information: application to SAR image segmentation

An image segmentation method based on optimized spatial texture information is proposed in this article. Spatial information, including the relative position of neighbouring pixels and texture features of the multiscale neighbourhood, is incorporated into the similarity measure of the fuzzy c-means (FCM) clustering algorithm, in which the Gaussian kernel is adopted to diminish the local incorrect segmentation. The FCM clustering is spatially adjusted and optimized by the particle swarm optimization (PSO) algorithm. The purpose of optimization is to obtain the appropriate control parameters influencing spatial information, which can improve segmentation results. Experimental results demonstrate that the proposed method achieves better segmentation performance and is capable of effectively segmenting synthetic images and synthetic aperture radar (SAR) images.     

Probabilistic models for robot-based object segmentation

This paper introduces a novel probabilistic method for robot based object segmentation. The method integrates knowledge of the robot’s motion to determine the shape and location of objects. This allows a robot with no prior knowledge of its workspace to isolate objects against their surroundings by moving them and observing their visual feedback. The main contribution of the paper is to improve upon current methods by allowing object segmentation in changing environments and moving backgrounds. The approach allows optimal values for the algorithm parameters to be estimated. Empirical studies against alternatives demonstrate clear improvements in both planar and three dimensional motion.     

An adaptive scale estimating method of multiscale image segmentation based on vector edge and spectral statistics information

ABSTRACT

Scale computation for multiscale image segmentation has become one of the key scientific problems in urgent need to be solved in the field of geographic object-based image analysis (GEOBIA). Due to the complexity of High Spatial Resolution Remote-Sensing Imagery (HSRRSI) data itself and the scale distribution differences among geographic features, it is difficult to effectively design a global scale parameter model to guide parameters setting in large scale regions and automatically produce an acceptable segmentation result simultaneously. Utilizing the vector edge and spectral statistics information, an adaptively global scale computation method named Global Scale Computation with Vector Edge (GSCVE) has been developed for multiscale segmentation, which is firstly proposed and implemented on mean-shift segmentation algorithm as an example. The highlight of the GSCVE algorithm is that it can calculate global scale parameters for multiscale image segmentation adaptively. The validity of GSCVE algorithm was verified directly by taking GeoEye and QuickBird images as segmentation experiments sample data, respectively. In addition, comparing with the renowned eCognition® multiscale segmentation algorithm, the relative advantages of GSCVE algorithm with adaptive property and the concurrence segmentation results of large and small scale geographic features are illustrated by the visual evaluation experiments simultaneously.     

Variation-based approach to image segmentation Variation-based approach to image segmentation

A new approach to image segmentation is presented using a variation framework. Regarding the edge points as interpolating points and minimizing an energy functional to interpolate a smooth threshold surface it carries out the image segmentation. In order to preserve the edge information of the original image in the threshold surface, without unduly sharping the edge of the image, a non-convex energy functional is adopted. A relaxation algorithm with the property of global convergence, for solving the optimization problem, is proposed by introducing a binary energy. As a result the non-convex optimization problem is transformed into a series of convex optimization problems, and the problem of slow convergence or nonconvergence is solved. The presented method is also tested experimentally. Finally the method of determining the parameters in optimizing is also explored.     

Nonparametric image segmentation

Image segmentation is reduced to quantisation which in tum is reduced to function approximation. The function approximation problem is formulared and solved as a global optimisation problem requiring neither any parametric assumptions nor parametric input, except the number of desired segment classes of the image. These are characterised by different colours or grey values. The quantisation approach is overlayed with an iteration scheme in accordance with the notion of so-called stable extrema of functions. This leads to segmentations of considerable robustness.This work was partially supported by project D 3 (http://www.uni ulm.de/SFB527/Projects/d3.html) of the SFB 527 sponsored by the Deutsche Forschungsgemeinschaft