A multi-temporal masking classification method for vineyard monitoring in central Spain

Abstract

The objective of image segmentation in remote sensing is to define regions in an image that correspond to objects in the ground scene. Traditional scene models underlying image segmentation procedures have assumed that objects as manifest in images have internal variances that are both low and equal. This scene model is unrealistically simple. An alternative scene model recognizes different scales of objects in scenes. Each level in the hierarchy is nested, or composed of objects or categories of objects from the preceding level. Different objects may have distinct attributes, allowing for relaxation of assumptions like equal variance.

A multiple-pass, region-based segmentation algorithm improves the segmentation of images from scenes better modelled as a nested hierarchy. A multiple-pass approach allows slow and careful growth of regions while inter-region distances are below a global threshold. Past the global threshold, a minimum region size parameter forces development of regions in areas of high local variance. Maximum and viable region size parameters limit the development of undesirably large regions.

Application of the segmentation algorithm for forest stand delineation in Landsat TM imagery yields regions corresponding to identifiable features in the landscape. The use of a local variance, adaptive-window texture channel in conjunction with spectral bands improves the ability to define regions corresponding to sparsely-stocked forest stands which have high internal variance.     

自然场景下多区域特征融合的混合航拍图像分割算法

混合图像分割算法所包含的两个部件中,初始分割不能形成低误分割率的过分割区域集,而区域合并存在缺少区域合并标号选择机制,且存在确定区域合并停止时刻的方式常不满足场景需求的不足。针对以上问题,提出一种基于多级区域信息融合的混合图像分割算法（MRIHS）。首先,使用改进的马尔可夫模型平滑超像素块,以形成初始分割区域;其次,在对初始分割区域进行相似性度量并选定待合并区域对后,利用设计出的区域标号选择机制来选定合并后的区域标号;最后,定义一种最佳合并状态以确定合并停止时刻。为验证MRIHS性能,在视觉对象类别（VOC）、剑桥驾驶标签视频数据库（CamVid）、自建的河湖巡检（rli）数据集上,将其与基于多维特征融合的混合图像分割算法（MFHS）、改进的基于区域合并的FCM图像分割算法（IFRM）、基于段间和边界均质性的混合图像分割算法（IBHHS）、基于多维色彩变换与一致性的混合图像分割算法（MCCHS）进行对比。结果表明：MRIHS在VOC、rli数据集上的边缘召回率（BR）、可达分割精准度（ASA）、查全率、重合率至少分别比其余算法提高了0.43个百分点、0.35个百分点、0.41个百分点、0.84个百分点;欠分割误差（UE）至少减少了0.65个百分点。在CamVid数据集上,MRIHS的查全率、重合率指标至少比其余算法提高了1.11个百分点、2.48个百分点。     

融合多特征的运动一致性图像分割

目的:在彩色图像分割中,光流法能够得到运动区域,但难以获得运动目标准确的分割边界,而常用的算法往往会产生过分割。为了克服光流法的不足,在保留显著性区域的同时抑制过分割,从而获得具有运动一致性区域的分割结果,提出融合多特征的运动一致性图像分割算法。方法:首先通过Mean Shift算法获取图像的初始分割,然后利用空域信息（包括颜色、边缘和区域面积）对视觉感知上具有相似性的区域进行合并,再利用时域信息进行运动一致性区域合并,最终得到分割结果。结果:实验结果表明通过结合时空信息,该方法能够有效抑制过分割,不仅弥补了光流场不能准确提取目标边缘的不足,而且提高了分割目标的完整性。结论:与两种流行的彩色图像分割算法相比,所提方法获得了更加理想的结果。     

一种利用灰度信息和二值信息的手写字符分割

在字符识别系统中,字符的有效分割是识别的关键。该文描述了一种两阶段的手写字符分割方法。根据字符倾斜角度将原字符串图像划分出若干个形状不规则的区域,每个区域含有一条分割线;针对现有分割方法的某些局限性,提出采用字符图像灰度信息和二值信息相结合的方式来求取分割区域中的分割路径。实验结果表明了该方法对于手写字符分割的有效性。     

基于上下文分析的无监督分层迭代算法用于SAR图像分割

基于聚类的分割算法能够有效地分析目标特征在特征域的分布结构,进而准确判断目标的所属类别,但难以利用图像的空间和边缘信息,而基于区域增长的分割算法能够在空间域利用多种图像信息计算目标之间的相似性,但缺乏对特征结构本身的深层挖掘,容易出现欠分割或过分割的结果. 本文结合这两种算法各自的优势,针对合成孔径雷达（Synthetic aperture radar,SAR）图像的特点,提出了一种基于上下文分析的无监督分层迭代算法. 该算法使用过分割区域作为操作单元,以提高分割速度,降低SAR图像相干斑噪声的影响. 在合并过分割区域时,该算法采用了分层迭代的策略：首先,设计了一种改进的模糊C均值聚类算法,对过分割区域的外观特征进行聚类分析,获得其类别标记,该类别标记包含了特征的分布结构信息. 然后,利用多种SAR图像特征对同类区域的空域上下文进行分析,使用区域迭代增长算法对全局范围内的相似区域进行合并,直到不存在满足合并条件的过分割区域对为止,再重新执行聚类算法. 这两种子算法分层交替迭代,扬长避短,实现了一种有效的方法来组织和利用多种信息对SAR图像进行分割. 对模拟和真实SAR图像的实验表明,本文提出的算法能够在区域一致性和细节保留之间做到很好的平衡,准确地分割出各类目标区域,对相干斑噪声具有很强的鲁棒性.     

Image segmentation using evolutionary computation

Image segmentation denotes a process by which a raw input image is partitioned into nonoverlapping regions such that each region is homogeneous and the union of any two adjacent regions is heterogeneous. A segmented image is considered to be the highest domain-independent abstraction of an input image. The image segmentation problem is treated as one of combinatorial optimization. A cost function which incorporates both edge information and region gray-scale uniformity is defined. The cost function is shown to be multivariate with several local minima. The genetic algorithm, a stochastic optimization technique based on evolutionary computation, is explored in the context of image segmentation. A class of hybrid evolutionary optimization algorithms based on a combination of the genetic algorithm and stochastic annealing algorithms such as simulated annealing, microcanonical annealing, and the random cost algorithm is shown to exhibit superior performance as compared with the canonical genetic algorithm. Experimental results on gray-scale images are presented     

Segmentation of small objects in color images

A method for effective segmentation of small objects in color images is presented. It can be used jointly with region growing algorithms. Segmentation of small objects in color images is a difficult problem because their boundaries are close to each other. The proposed algorithm accurately determines the location of the boundary points of closely located small objects and finds the skeletons (seed regions) of those objects. The method makes use of conditions obtained by analyzing the change of color characteristics of the edge pixels along the direction that is orthogonal to the boundaries of adjacent objects. These conditions are generalized for the case of the well-known class of color images having misregistration artifacts. If high-quality seed regions are available, the final segmentation can be performed using one of the region growing methods. The segmentation algorithm based on the proposed method was tested using a large number of color images, and it proved to be very efficient.     

A two step salient objects extraction framework based on image segmentation and saliency detection

Salient objects extraction from a still image is a very hot topic, as it owns a lot of useful applications (e.g., image compression, content-based image retrieval, digital watermarking). In this paper, targeted to improve the performance of the extraction approach, we propose a two step salient objects extraction framework based on image segmentation and saliency detection (TIS). Specially, during the first step, the image is segmented into several regions using image segmentation algorithm and the saliency map for the whole image is detected with saliency detection algorithm. In the second step, for each region, some features are extracted for the SVM algorithm to classify the region as a background region or a salient region twice. Experimental results show that our proposed framework can extract the salient objects more precisely and can achieve a good extraction results, compared with previous salient objects extraction methods.     

Multiresolution video object extraction fitted to scalable wavelet-based object coding

To enable content based functionalities in video processing algorithms, decomposition of scenes into semantic objects is necessary. A semi-automatic Markov random field based multiresolution algorithm is presented for video object extraction in a complex scene. In the first frame, spatial segmentation and user intervention determine objects of interest. The specified objects are subsequently tracked in successive frames and newly appeared objects/regions are also detected. The video object extraction algorithm includes discrete wavelet transform decomposition multiresolution Markov random field (MRF)-based spatial segmentation with emphasis on border smoothness at different resolutions, and an MRF-based backward region classification that determines the tracked objects in the scene. Finally, a motion constraint, embedded in the region classifier, determines the newly appeared objects/regions and completes the proposed algorithm towards an efficient video segmentation algorithm. The results are applicable for generic segmentation applications, however the proposed multiresolution video segmentation algorithm supports scalable object-based wavelet coding in particular. Moreover, compared to traditional object extraction algorithms, it produces smoother and more visually pleasing shape masks at different resolutions. The proposed effective multiresolution video object extraction method allows for larger motion, better noise tolerance and less computational complexity     

利用三维自适应分裂-合并的MRI图像分割算法设计

为了在医学图像分割中,发现均匀几何三维区域的复杂形状,以提高分割准确率,提出一种基于3D几何特征分裂-合并(ASM)的脑部MRI图像分割算法;首先构建简单平行六面体的12种3D区域分割策略,体积分割技术将整个体积划分为许多大的均匀三维几何区;然后,在体积内定义更多小的均匀区域,以便在随后的合并步骤中有更大的生存概率;最后,进行多级区域合并,合并阶段只涉及复杂ASM树的叶子,考虑灰度相似性和共同边界区的大小,将小的区域合并为大邻近区;相比其他几种MRI图像分割算法,提出的方法在分割过程对噪声具有鲁棒性,提高了分割性能和准确率;另外提出的方法不需要训练数据集。     

Urban road extraction from high-resolution optical satellite images

In recent years, many approaches have been exploited for automatic urban road extraction. Most of these approaches are based on edge and line detecting algorithms. In this paper, a new integrated system for automatic extraction of main roads in high-resolution optical satellite images is present. Firstly, a multi-scale greylevel morphological cleaning algorithm is proposed to reduce the grey deviation of the road regions. Secondly, based on the greylevel difference between road surfaces and environmental objects, a colour high-resolution satellite image is segmented into a simplified imagemap by using the mean shift algorithm, which consists of three stages. The first stage deals with image filtering, the second stage deals with colour segmentation, and the third stage is proposed to fuse small regions in the segmented image. The mean shift filter algorithm not only smoothes the image, but also preserves abrupt changes (i.e. edges) in the local structure. The mean shift segmentation algorithm is a straightforward extension of the smoothing algorithm, which preserves discontinuity. From the histogram of the simplified imagemap, we can find the potential road surfaces, and use greylevel threshold to convert the segmented image into a binary one. The binary image is processed by using binary mathematical morphological closing and opening to remove small objects and to open the connected street blocks. We use a contour tracing algorithm to remove holes in street-block regions and to detect the street blocks' contours. In this research we found that many street blocks' contours were preserved perfectly, except for some of them which were depressed. Finally, we utilize the convex hull algorithm to smooth the street blocks' zigzag edges and to close the gaps in some street blocks, and then, we get the road edges. The integrated system for road network extraction is tested on the red band of an IKONOS multispectral image; all algorithms in this study are developed in C++ under Windows XP operating system. Results of the road network extraction are presented to illustrate the validation of the extracting strategy and the corresponding algorithms in this research, and future prospects are exposed.     

Improving image segmentation based on patch-weighted distance and fuzzy clustering

Image segmentation is the basis of image analysis, object tracking, and other fields. However, image segmentation is still a bottleneck due to the complexity of images. In recent years, fuzzy clustering is one of the most important selections for image segmentation, which can retain information as much as possible. However, fuzzy clustering algorithms are sensitive to image artifacts. In this study, an improved image segmentation algorithm based on patch-weighted distance and fuzzy clustering is proposed, which can be divided into two steps. First, the pixel correlation between adjacent pixels is retrieved based on patch-weighted distance, and then the pixel correlation is used to replace the influence of neighboring information in fuzzy algorithms, thereby enhancing the robustness. Experiments on simulated, natural and medical images illustrate that the proposed schema outperforms other fuzzy clustering algorithms.

     

Image segmentation by probabilistic bottom-up aggregation and cue integration

We present a bottom-up aggregation approach to image segmentation. Beginning with an image, we execute a sequence of steps in which pixels are gradually merged to produce larger and larger regions. In each step, we consider pairs of adjacent regions and provide a probability measure to assess whether or not they should be included in the same segment. Our probabilistic formulation takes into account intensity and texture distributions in a local area around each region. It further incorporates priors based on the geometry of the regions. Finally, posteriors based on intensity and texture cues are combined using “a mixture of experts” formulation. This probabilistic approach is integrated into a graph coarsening scheme, providing a complete hierarchical segmentation of the image. The algorithm complexity is linear in the number of the image pixels and it requires almost no user-tuned parameters. In addition, we provide a novel evaluation scheme for image segmentation algorithms, attempting to avoid human semantic considerations that are out of scope for segmentation algorithms. Using this novel evaluation scheme, we test our method and provide a comparison to several existing segmentation algorithms.     

Multi-scale energy optimization for object proposal generation

In this paper, we present an object proposal generation method by applying energy optimization into superpixel merging algorithms in a multiscale framework, which could generate possible object locations in one image. As images in object detection datasets always enjoy high diversity, we adopt two different energy functions with multi-scales. Thus, our method enjoys the strength of global search, which is strong in locating salient object by concerning the whole image at one merge iteration, as well as the strength of local search which is more likely to recall the un-salient instances. What’s more, unlike most superpixel merging algorithms that are based on diversified segmentation results, our approach takes advantage of robust edge detection and segments each image only once, which greatly reduces the number of proposals. Experiments on PASCAL VOC 2007 test set show that the proposed method outperforms most previous superpixel merging based methods and also could compete with state-of-the-art proposal generators.     

基于Mean Shift和FDoG的图像卡通化渲染

针对现有图像卡通化渲染算法区域划分不明显或提取的边界不够连贯的问题,提出了一种基于Mean Shift和FDoG的图像卡通化渲染方法。该方法通过区域分割与边界处理相融合的手段,获取区域明晰、边界光滑连贯的卡通对象,同时结合亮度量化策略等后处理技术净化对象,最终获得具有卡通效果的图像。渲染算法采用Mean Shift技术对图像进行分割,通过设置合适的参数获取若干有意义的区域;引入FDoG算法思想对图像边界进行分析和提取;最后去除或合并视觉上的干扰区域,并参考卡通画的亮度分布特点对图像进行亮度量化,得到最终的卡通风格图像。方法实现简单,自动化程度较高,实验结果较理想。     

利用Kolmogorov-Smirnov统计的区域化图像分割

目的 为了在未知或无法建立图像模型的情况下,实现统计图像分割,提出一种结合Voronoi几何划分、K-S(Kolmogorov-Smirnov)统计以及M-H(Metropolis-Hastings)算法的图像分割方法.方法 首先利用Voronoi划分将图像域划分成不同的子区域,而每个子区域为待分割同质区域的一个组成部分,并利用K-S统计定义类属异质性势能函数,然后应用非约束吉布斯表达式构建概率分布函数,最后采用M-H算法进行采样,从而实现图像分割.结果 采用本文算法,分别对模拟图像、合成图像、真实光学和SAR图像进行分割实验,针对模拟图像和合成图像,分割结果精度均达到98%以上,取得较好的分割结果.结论 提出基于区域的图像分割算法,由于该算法中图像分割模型的建立无需原先假设同质区域内像素光谱测度的概率分布,因此提出算法具有广泛的适用性.为未知或无法建立图像模型的统计图像分割提供了一种新思路.     

关于多种分割方法的综合集成的研究

图像分割是图像处理的一个重要领域,本文尝试从一个新的角度来理解与研究图像分割的方法,提出将多种分割方法加以综合集成的思路。它包含了三个方面的内容：将图像作合理区域划分;区域分割算法的优选;区域间分割结果的平滑过渡。     

一种基于区域分级合并的彩色图像分割方法

在区域合并过程中,手工设置颜色相似性和边界距离的权重极大地影响了分割的精度和自动化.针对这一问题,提出了一种新的基于区域分级合并的彩色图像分割算法.该方法能够根据邻接区域的边界特点设置权重因子,从而自适应地融合区域的颜色相似性和边界距离.使用均值漂移算法对图像进行初始分割,将原图像分割为具有较好边界的同质区域;通过计算区域相似度对区域进行分级合并.多幅彩色图像的分割实验结果证明,所提算法优于传统的基于区域合并的方法.     

模糊相关图割的非监督层次化彩色图像分割

目的 基于阈值的分割方法能根据像素的信息将图像划分为同类的区域,其中常用的最大模糊相关分割方法,因能利用模糊相关度量划分的适当性,得到较好的分割结果,而广受关注。然而该算法存在划分数需预先确定,阈值的分割结果存在孤立噪声,无法对彩色图像实施分割的问题。为此,提出基于模糊相关图割的非监督层次化分割策略来解决该问题。方法 算法首先将图像划分为若干超像素,以提高层次化图像分割的效率;随后将快速模糊相关算法与图割结合,构成模糊相关图割2-划分算子,在确保分割效率的基础上,解决单一阈值分割存在孤立噪声的问题;最后设计了自顶向下层次化分割策略,利用构建的2-划分算子选择合适的区域及通道,迭代地对超像素实施层次化分割,直到算法收敛,划分数自动确定。结果 对Berkeley分割数据库上300幅图像进行了测试,结果表明算法能有效分割彩色图像,分割精度优于Ncut、JSEG方法,运行时间较这两种方法也提高了近20%。结论 本文算法为最大模糊相关算法在非监督彩色图像分割领域的应用提供指导依据,能用于目标检测和识别领域。